Вакансии

Location: Hong Kong, Hong Kong SAR Who We Are At OKX, we believe that the future will be reshaped by crypto, and ultimately contribute to every individual's freedom. OKX is a leading crypto exchange, and the developer of OKX Wallet, giving millions access to crypto trading and decentralized crypto applications (dApps). OKX is also a trusted brand by hundreds of large institutions seeking access to crypto markets. We are safe and reliable, backed by our Proof of Reserves. Across our multiple offices globally, we are united by our core principles: We Before Me , Do the Right Thing , and Get Things Done . These shared values drive our culture, shape our processes, and foster a friendly, rewarding, and diverse environment for every OK-er. OKX is part of OKG, a group that brings the value of Blockchain to users around the world, through our leading products OKX, OKX Wallet, OKLink and more. About the Opportunity As an Infra Product Manager, you will be responsible for the design, development, and lifecycle management of Web3 cross-business infrastructure products, with a focus on core components such as central data services, market data systems, labeling systems, asset category management, and algorithm platforms for Wallet, DEX and other businesses. You will collaborate closely with engineering, data, and business teams to ensure products meet the performance, security, and scalability requirements of Web3 businesses. This role demands strong product thinking, technical depth, and insight into the Web3 industry to help the team build an efficient and reliable Web3 Infra system. What You’ll Be Doing Central Data Services Management : Oversee the design of central data services for Web3 cross-business domains, including data collection, integration, storage, querying, and distribution. Ensure real-time data accuracy, security, and support for complex business needs and multi-platform environments. Market Data System Development and Optimization : Contribute to the product design and iteration of a universal market data system, delivering real-time market data (e.g., price, trading volume, liquidity metrics). Support API and front-end integration while optimizing system performance for high-concurrency scenarios. AI Trading Products : Able to design DEX AI Trading Products based on trading strategies, meme tokens, smart money signals, and leverage the latest AI technologies (AI Agent, LLM etc.) DeFi Native: Have a solid understanding of Web3, including its core concepts, underlying blockchain technologies, and emerging ecosystems such as DeFi, NFTs, DAOs, and Layer 2 solutions. Stay closely informed about the latest industry trends, market dynamics, and technological developments, with the ability to quickly assess and adapt to new protocols, products, and narrative shaping space. Onchain Data and Smart Contract Analysis : Conduct research on smart contracts, understand how Dapps and protocol works, design and improve product infra performance based on the protocol underlying mechanics, make sense of the onchain data whenever necessary. Algorithm Development and Improvement : Design and iterate an algorithm to support on-chain data analytics, trading signal discovery, and risk control scenarios. Manage the integration, testing, and deployment of algorithm models to ensure efficiency and reliability of outputs. Collaborate with data analysts and data scientists to integrate machine learning or on-chain analytics models, elevating the intelligence of Infra products. What We Look For In You Education : Bachelor’s degree or above in Computer Science, Software Engineering, or a related field. Work Experience : At least 5 years of experience as a Product Manager. Preference for candidates with Web3 industry experience or expertise in infrastructure products (e.g., data services, labeling platforms, algorithm platforms). Technical Skills : Experience with blockchain fundamentals, including smart contracts, on-chain data analysis, consensus mechanisms, and cross-chain technologies. Familiar with onchain and web3 ecosystems, a genuine passion for the crypto industry and proactive engagement with the community are highly valued. Proficiency in product management and system-design thinking is strongly preferred. Proficiency in AI technology, Agent, LLM, and AI application experience is a huge plus. Basic knowledge of algorithm platforms; familiarity with machine learning models or data analytics workflows is preferred. Soft Skills : Excellent communication and collaboration skills, with the ability to effectively bridge business and technical teams. Strong user-oriented mindset, capable of designing products based on user pain points. Robust problem-solving skills and the ability to make quick decisions in uncertain environments. Fluent in English and Chinese. Nice to Haves Knowledge of Web3-related products and deep technical understanding of blockchain, smart contracts, and related technologies. Experience with on-chain trading and an understanding of on-chain opportunity discovery processes. Familiarity with tokenomics and an understanding of economic incentive mechanisms in the Web3 space. Perks & Benefits Competitive total compensation package L&D programs and Education subsidy for employees' growth and development Various team building programs and company events Wellness and meal allowances Comprehensive healthcare schemes for employees and dependants More that we love to tell you along the process! Please note that Hong Kong is a group-level service hub, and OKX does not carry on a business of operating a virtual asset trading platform in Hong Kong #LI-MX1 #LI-ONSITE Notice: All official OKX vacancies are published on this website. While roles may appear on selected third-party platforms from time to time, information on other sites may be inaccurate or outdated. If in doubt, please apply directly through our official careers website. Information collected and processed as part of the recruitment process of any job application you choose to submit is subject to OKX 's Candidate Privacy Notice .

Location: Singapore, Singapore Who We Are At OKX, we believe that the future will be reshaped by crypto, and ultimately contribute to every individual's freedom. OKX is a leading crypto exchange, and the developer of OKX Wallet, giving millions access to crypto trading and decentralized crypto applications (dApps). OKX is also a trusted brand by hundreds of large institutions seeking access to crypto markets. We are safe and reliable, backed by our Proof of Reserves. Across our multiple offices globally, we are united by our core principles: We Before Me , Do the Right Thing , and Get Things Done . These shared values drive our culture, shape our processes, and foster a friendly, rewarding, and diverse environment for every OK-er. OKX is part of OKG, a group that brings the value of Blockchain to users around the world, through our leading products OKX, OKX Wallet, OKLink and more. About the Opportunity As an Infra Product Manager, you will be responsible for the design, development, and lifecycle management of Web3 cross-business infrastructure products, with a focus on core components such as central data services, market data systems, labeling systems, asset category management, and algorithm platforms for Wallet, DEX and other businesses. You will collaborate closely with engineering, data, and business teams to ensure products meet the performance, security, and scalability requirements of Web3 businesses. This role demands strong product thinking, technical depth, and insight into the Web3 industry to help the team build an efficient and reliable Web3 Infra system. What You’ll Be Doing Central Data Services Management : Oversee the design of central data services for Web3 cross-business domains, including data collection, integration, storage, querying, and distribution. Ensure real-time data accuracy, security, and support for complex business needs and multi-platform environments. Market Data System Development and Optimization : Contribute to the product design and iteration of a universal market data system, delivering real-time market data (e.g., price, trading volume, liquidity metrics). Support API and front-end integration while optimizing system performance for high-concurrency scenarios. AI Trading Products : Able to design DEX AI Trading Products based on trading strategies, meme tokens, smart money signals, and leverage the latest AI technologies (AI Agent, LLM etc.) DeFi Native: Have a solid understanding of Web3, including its core concepts, underlying blockchain technologies, and emerging ecosystems such as DeFi, NFTs, DAOs, and Layer 2 solutions. Stay closely informed about the latest industry trends, market dynamics, and technological developments, with the ability to quickly assess and adapt to new protocols, products, and narrative shaping space. Onchain Data and Smart Contract Analysis : Conduct research on smart contracts, understand how Dapps and protocol works, design and improve product infra performance based on the protocol underlying mechanics, make sense of the onchain data whenever necessary. Algorithm Development and Improvement : Design and iterate an algorithm to support on-chain data analytics, trading signal discovery, and risk control scenarios. Manage the integration, testing, and deployment of algorithm models to ensure efficiency and reliability of outputs. Collaborate with data analysts and data scientists to integrate machine learning or on-chain analytics models, elevating the intelligence of Infra products. What We Look For In You Education : Bachelor’s degree or above in Computer Science, Software Engineering, or a related field. Work Experience : At least 5 years of experience as a Product Manager. Preference for candidates with Web3 industry experience or expertise in infrastructure products (e.g., data services, labeling platforms, algorithm platforms). Technical Skills : Experience with blockchain fundamentals, including smart contracts, on-chain data analysis, consensus mechanisms, and cross-chain technologies. Familiar with onchain and web3 ecosystems, a genuine passion for the crypto industry and proactive engagement with the community are highly valued. Proficiency in product management and system-design thinking is strongly preferred. Proficiency in AI technology, Agent, LLM, and AI application experience is a huge plus. Basic knowledge of algorithm platforms; familiarity with machine learning models or data analytics workflows is preferred. Soft Skills : Excellent communication and collaboration skills, with the ability to effectively bridge business and technical teams. Strong user-oriented mindset, capable of designing products based on user pain points. Robust problem-solving skills and the ability to make quick decisions in uncertain environments. Fluent in English and Chinese. Nice to Haves Knowledge of Web3-related products and deep technical understanding of blockchain, smart contracts, and related technologies. Experience with on-chain trading and an understanding of on-chain opportunity discovery processes. Familiarity with tokenomics and an understanding of economic incentive mechanisms in the Web3 space. Perks & Benefits Competitive total compensation package L&D programs and Education subsidy for employees' growth and development Various team building programs and company events Wellness and meal allowances Comprehensive healthcare schemes for employees and dependants More that we love to tell you along the process! #LI-MX1 #LI-ONSITE Notice: All official OKX vacancies are published on this website. While roles may appear on selected third-party platforms from time to time, information on other sites may be inaccurate or outdated. If in doubt, please apply directly through our official careers website. Information collected and processed as part of the recruitment process of any job application you choose to submit is subject to OKX 's Candidate Privacy Notice .

Location: Paris; San Francisco; London; New York; Toronto; Montreal Workplace: Remote WHO ARE WE? Cohere is the leading security-first enterprise AI company. We build cutting-edge foundation AI models and end-to-end products that are designed to solve real-world business problems. We’re training and deploying frontier models for enterprises who are building AI systems. We believe that our work is instrumental to the widespread adoption of AI and we are looking for folks that want to be part of that. We obsess over what we build. Each one of us is responsible for contributing to increasing the capabilities of our models and the value they drive for our customers. Cohere is a team of researchers, engineers, designers, and more, who are all passionate about their craft. We are a global technology company headquartered in Toronto with key offices in London, New York City, San Francisco, Montreal, Paris, Berlin and Seoul. Join us! Role Overview: Contribute in and provide strong support for model training pipelines, ship state of the art models to production, and bridge the gap between research and production. We have one of the highest ratio of compute to engineers in the world. We do not delineate strongly between engineering and research. Everyone will contribute to writing production code and supporting our research effort depending on individual interest and organizational needs. We have all the compute, data, and talent available for you to do your best work. Please Note: We have offices in London, Paris, Toronto, San Francisco and New York but also embrace being remote-friendly! There are no restrictions on where you can be located for this role. Key Responsibilities: - Design and write high-performant and scalable software for training.  - Improve our training setup from an infrastructure and codebase performance standpoint. - Craft and implement tools to speed up our training cycles and improve the overall efficacy of our training infrastructure - Research, implement, and experiment with ideas on our supercompute and data infrastructure. - Learn from and work with the best researchers in the field. Qualifications: - Extremely strong software engineering skills. - Proficiency in Python and related ML frameworks such as JAX, Pytorch and XLA/MLIR. - Experience with distributed training infrastructures (Kubernetes, Slurm) and associated frameworks (Ray) - Experience using large-scale distributed training strategies. - Hands on experience on training large model at scale and having contributed to the tooling and/or setup of the training infrastructure - Bonus: paper at top-tier venues (such as NeurIPS, ICML, ICLR, AIStats, MLSys, JMLR, AAAI, Nature, COLING, ACL, EMNLP). Working Location: This role can be based remotely or from one of our office locations listed on the job description - there is no minimum in-office qualification requirement. We care most about hiring exceptional people regardless of locations, though please check the location listed on the posting for guidance around the core time zone or working hours alignment expected for the role. FULL-TIME EMPLOYEES AT COHERE ENJOY THESE PERKS: - A weekly lunch stipend of $75/£75 or equivalent in your local currency for lunch. - Full health and dental benefits, including a separate budget for mental health. - RRSP matching, 401K, Pension Scheme. - 100% Parental Leave top-up for up to 6 months, for either parent. - Annual enrichment benefits: Arts & culture, fitness/wellness, quality time, and a workspace improvement credit. Education & learning stipend for conferences, courses, and coaching. - 6 weeks of paid vacation (30 working days!) - Budget for traveling to other offices if you are remote, plus an annual company offsite. HOW AND WHERE WE WORK: - Cohere is remote-friendly, but we also have offices in Toronto, London, New York City, San Francisco, Montreal, Paris, Berlin and Seoul with more opening soon. - For those in the office: a daily lunch program, plenty of snacks, and regular community and social events. - For those not near an office: a co-working benefit so you can work alongside others in your city. - Everyone receives a $500 home office stipend to set up your workspace properly. If any of the above doesn’t line up exactly with your experience, we still encourage you to apply. We strive to create an inclusive work environment for all; we welcome applicants from all backgrounds and are committed to providing equal opportunities. Should you require any accommodations during the recruitment process, please submit an Accommodations Request Form https://docs.google.com/forms/d/12a6IrLdF3kI2nonKSr4tiFuz18rLQbaeYV-JM9L4o9Q/edit, and we will work together to meet your needs. We may use AI-enabled tools to screen and assess applicants against the criteria for this position. This helps our recruiters identify potentially qualified candidates, but it doesn't limit the applications our recruiters may review or consider. Beware of Scams: Cohere will never ask for payment or third-party services (e.g., CV writing) as part of our hiring process. All legitimate roles are listed on the Cohere careers page and LinkedIn only, with all communications from Cohere employees coming from an @cohere.com or @cw.cohere email alias. If jobs are viewed on other sites then please verify these through our official careers https://cohere.com/careers page.

Location: Remote (United States) Workplace: Remote This is a remote position available anywhere in the world! Linkedin makes us show a country, but we hire the best people wherever they are. At Railway, we make it simple to ship anything. A lot goes into that. This role bridges the universe of atoms, to the universe of bits. We’ve built ground up infrastructure to deliver better-than-best-in-class experience to all of our users, by building datacenters all over the world. They’re designed for performance of course, but also for resilience and reliability. Doesn’t matter how good your stuff is if it doesn’t stay up right? “The person who says it cannot be done, should not interrupt the person doing it” - Confucius Curious? Learn more in our blog post about this team and the great work they’re doing: So You Want to Build Your Own Data Center https://blog.railway.com/p/data-center-build-part-one Want to learn about our work culture? Here is a three-part blog series that will help you see the unique ways our team works (1 https://blog.railway.app/p/how-we-workand 2 https://blog.railway.app/p/how-we-work-volume-ii and 3 https://blog.railway.app/p/how-we-work-volume-iii). REQUIRED QUALIFICATIONS/EXPERIENCE Expect to need all of these, day to day, to excel at Railway 1. Built application level code (Golang, GRPC, Postgres, etc) 2. Made use of OS primitives (syscalls, cgroups, etc) 3. Delivered projects 0→N. Lead it, scaled it, seen your “brilliant solutions” become tech debt, etc 4. Operated with agency and urgency (bonus points if remotely!) 5. Had enough foresight to ship for today, design for 10x, and engineer for 100x in 18mo 6. Communicated clearly using RFCs/Docs/etc to break up + deliver large, nebulous tasks 7. Been oncall and know that you will be 1-2 weeks per quarter 8. Constructed monitoring, alerts, and dashboards to notify you before things break 9. Built the confidence to say no to 99% of things, and the grit to deliver the 1% that matter JOB DESCRIPTION At Railway, we make it simple to ship anything. A lot goes into that. This role bridges the universe of atoms, to the universe of bits. We’ve built ground up infrastructure to deliver better-than-best-in-class experience to all of our users, by building datacenters all over the world. They’re designed for performance of course, but also for resilience and reliability. Doesn’t matter how good your stuff is if it doesn’t stay up right? “The person who says it cannot be done, should not interrupt the person doing it” - Confucius Curious? Learn more in our blog post about this team and the great work they’re doing: So You Want to Build Your Own Data Center https://blog.railway.com/p/data-center-build-part-one Want to learn about our work culture? Here is a three-part blog series that will help you see the unique ways our team works (1 https://blog.railway.app/p/how-we-workand 2 https://blog.railway.app/p/how-we-work-volume-ii and 3 https://blog.railway.app/p/how-we-work-volume-iii). Required Qualifications/Experience Expect to need all of these, day to day, to excel at Railway 1. Built application level code (Golang, GRPC, Postgres, etc) 2. Made use of OS primitives (syscalls, cgroups, etc) 3. Delivered projects 0→N. Lead it, scaled it, seen your “brilliant solutions” become tech debt, etc 4. Operated with agency and urgency (bonus points if remotely!) 5. Had enough foresight to ship for today, design for 10x, and engineer for 100x in 18mo 6. Communicated clearly using RFCs/Docs/etc to break up + deliver large, nebulous tasks 7. Been oncall and know that you will be 1-2 weeks per quarter 8. Constructed monitoring, alerts, and dashboards to notify you before things break 9. Built the confidence to say no to 99% of things, and the grit to deliver the 1% that matter Desired Qualifications/Experience Expect to go deep in 1-2 of these to do the best work of your life 1. Designed and procured $1m+ hardware for datacenters 2. Managed and orchestrated supply chains globally 3. Designed rack level layouts for optimal efficiency (heat/energy/etc) 4. Built automated systems for inventory management up to PXE bootstrapping We value and love to work with diverse persons from all backgrounds Things to know For better or worse, we're a startup; our team dynamics are different from companies of different sizes and stages. - We're distributed ALL across the globe, and that's only going to be more and more distributed. As a result, stuff is ALWAYS happening. - We do NOT expect you to work all the time, but you'll have to be diligent about your boundaries because the end of your day may overlap with the start of someone else's. - We're a small team, with high ownership, who are not only passionate about what we do, but seek to be exceptional as well. At the time of writing we're 21, serving hundreds of thousands of users. There's a lot of stuff going on, and a lot of ambiguity. - We want you to own it. We believe that ownership is a key to growth, and part of that growth is not only being able to make the choices, but owning the success, or failure, that comes with those choices. Benefits and perks At Railway, we provide best in class benefits. Great salary, full health benefits including dependents, strong equity grants, equipment stipend, and much more. For more details, check back on the main careers page https://railway.app/careers/full-stack. Beyond compensation, there are a few things that we believe that make working at Railway truly unique: - Autonomy: We have very few meetings. Just a Monday and a Friday to go over the Company Board. We think your time is sacred, whether it's at work, or outside of work. - Ownership: We're a company with a high ownership, high autonomy culture. We hope that you'll come in, help us, and over the course of many years do the best work of your life. When we bring you onboard, we expect you to change the company. - Novel problems/solutions: We're a startup that's well funded, with cool problems, which lets us implement novel solutions! We abhor “busywork” and think, whether it's community, engineering, operations, etc there's always opportunity for creative and high leverage solutions. - Growth: We want you to grow with us, but we know that talent is loaned, so when you figure out what area you want to grow in next, whether it's at Railway or outside, we'll make sure you land there. How we hire No tricks. No surprises. Here's the entire process: 1. Talk with us about the role - This is completely open ended and we're just trying to see who you are, what you want to do, and where you wanna go. 2. Work on a small project to discuss in the interview - Asynchronously implement the following: - Pre-interview: Design a datacenter procurement, buildout, and management process from scratch - You can, and SHOULD! ask us questions ahead of time. 3. Review your solution with the Team 1. You'll sit down with someone on the team and go over the above. We'll poke into your solution, as well as get you acquainted with two more members of the team. 1. Looking for: Learn about your problem solving skills. How you break down a problem and how you present a solution. 2. Interview Structure (60 Minutes): 1. Pre-work (before your interview): Complete your solution (advised) 2. 0-5m: introduction 3. 5-50m: Building (or expanding) your solution 4. 50-60m: Questions on Railway/Tech/etc 4. Meet the Team 1. You'll meet the Team, which will be comprised of 4 people from vastly different sections of the company. 1. Looking for: How you work with the rest of the team and communicate. 5. Offer and Details Chat with CEO 1. Finally, we will go over the process, the role, and hammer out the details about your position, onboarding, and all the deets. #Global

Location: San Francisco, California Workplace: Hybrid ABOUT SENTRY Software runs the world and the pace is faster than ever. Sentry helps developers fix errors and performance issues before users notice, so teams can spend less time firefighting and more time building. Trusted by 200,000+ organizations, Sentry is today’s application monitoring standard and our team is building its AI-native future. ABOUT THE ROLE Sentry's Infrastructure Engineering team is what makes operating Sentry simple, safe, and seamless for every other engineering team in the company. They build the internal control platforms, configuration systems, traffic routing, and automation that let product engineers operate services safely at scale without needing deep infrastructure expertise themselves. As the Engineering Manager for Infrastructure Engineering, you'll lead a team of engineers building the tools that power Sentry's growth: internal admin and change management tools, configuration automation, and the routing layer that underlies Sentry's architecture. You'll be responsible for technical vision, team health, system reliability, and partnership with engineering teams across the company who depend on your team's tools every day. You'll work closely with leaders across Infrastructure, Platform, and Production Engineering to shape how Sentry scales its operational model as the company grows. IN THIS ROLE YOU WILL - Lead a team of engineers building the internal control platforms that every engineering team at Sentry relies on to operate services safely. - Drive the evolution of Infrastructure Engineering's platform, including configuration management, traffic routing and environment controls - Own the team's technical direction, contributing to key decisions on API architecture, internal tooling design, and automation frameworks. - Nurture and grow engineers at different levels, providing support through coaching, mentorship, and career development. - Foster an inclusive, high-performing team culture focused on ownership, learning, and delivery. - Partner with Production Engineering, SRE, and Developer Infrastructure to make sure operational tooling stays consistent and well-scoped across the org. - Own the reliability, adoption, and roadmap of the team's tools while building a culture of technical excellence and pragmatic decision making. YOU'LL LOVE THIS JOB IF YOU - Are passionate about building tools that make operating complex systems simple for other engineers. - Enjoy solving infrastructure problems around configuration, routing, and automation, and turning them into clean, self-service abstractions. - Believe in using proven technologies and committing to a single, reliable choice rather than chasing novelty. - Think in terms of building for scale and cost effectiveness from day one, and for the needs of many teams rather than one. - Value cross-functional collaboration and thrive in hybrid, in-person settings with engineers across functions, levels, and geographies. - Take pride in building strong, healthy teams and enabling them to lead high-impact, multi-year initiatives. - Want to be part of a thoughtful, highly technical team that cares about long-term system health and operational simplicity. QUALIFICATIONS - 10+ years of software engineering experience, ideally with a focus on internal tooling, infrastructure automation, platform engineering, or distributed systems. - 4+ years of engineering management experience, including growing and managing teams of engineers. - Experience as a Tech Lead or Engineering Manager leading the architecture and long-term evolution of internal control platforms or similar systems. - Strong backend or full-stack engineering background, with solid Python skills. - Familiarity with Kubernetes, terraform, python, and a systems mindset for abstracting complex infrastructure for others to use. - Strong technical communication skills and a track record of effective cross-team and leadership collaboration. - Proven experience delivering and owning multi-year engineering initiatives in a leadership or technical leadership capacity. - Comfortable working across org boundaries and aligning priorities across infra, platform, and product teams. The base salary range that Sentry reasonably expects to pay for this position is $220,000 to $400,000 USD, plus an annual performance bonus of up to 15% of base salary. A successful candidate’s actual base salary (or hourly wage) amount will be determined by a variety of relevant factors including, without limitation, the candidate’s work location, education, work and other relevant experience, skills, and job-related knowledge. A successful candidate will be eligible to participate in Sentry’s employee benefit plans/programs applicable to the candidate’s position (including incentive compensation, equity grants, paid time off, and group health insurance coverage). See Sentry Benefits https://sentry.io/careers/ for more details about the Company’s benefit plans/programs. EQUAL OPPORTUNITY AT SENTRY Sentry is committed to providing equal employment opportunities to its employees and candidates for employment regardless of race, color, ancestry, religion, sex, national origin, sexual orientation, age, citizenship, marital status, disability, gender identity, veteran status, or other legally-protected characteristic. This commitment includes the provision of reasonable accommodations to employees and candidates for employment with physical or mental disabilities who require such accommodations in order to (a) perform the essential functions of their jobs, or (b) seek employment with Sentry. We strive to build a diverse team, with an inclusive culture where every teammate can thrive. Sentry is an open-source company because we believe that everyone, everywhere, should have the ability and tools to make great software. Software should be accessible. That starts with making our industry accessible. If you need assistance or an accommodation due to a disability, you may contact us at accommodations@sentry.io. Want to learn more about how Sentry handles applicant data? Get the details in our Applicant Privacy Policy https://sentry.io/careers/applicantprivacy/.

Location: San Francisco, CA Workplace: OnSite ABOUT US At LangChain, our mission is to enable every company to own its intelligence. We believe agents will transform how companies operate, and we're building the foundation to make that future possible. Our open-source harnesses and LangSmith platform give developers and enterprises the infrastructure to run, improve, and govern agents at scale. We've raised $460M to date, with our most recent funding round valuing the company at $3B, backed by leading investors including Sequoia, Benchmark, IVP, and CapitalG. As we continue to expand our platform and tackle some of the hardest challenges in agent engineering, every team member has the opportunity to make a meaningful impact and help shape both our technology and the future of the industry. More than 7,000 organizations rely on LangSmith, from fast-growing startups to Fortune 500 enterprises, including NVIDIA, Bridgewater, LinkedIn, Workday, and Rippling. We're just getting started on making high-performing agents easier to build and manage at scale. About LangChain At LangChain, our mission is to make intelligent agents ubiquitous. We provide the agent engineering platform and open source frameworks developers need to ship reliable agents fast. Our open source frameworks, LangChain and LangGraph, see over 90+ million downloads per month and help developers build agents with speed and granular control. LangSmith offers observability, evaluation, and deployment for rapid iteration, enabling teams to transform LLM systems into dependable production experiences. LangChain is trusted by millions of developers worldwide and powers AI teams at companies like Replit, Clay, Cloudflare, Harvey, Rippling, Vanta, Workday, and more.   About the role You’ll be the hands‑on security lead embedded with core product teams to secure agentic workloads end‑to‑end, from SDK through LangSmith/Graph services and customer integrations. You’ll define our security roadmap, land immediate hardening wins, and raise the bar on how AI infra is protected in production. - Own product & platform security: Design and drive application/infrastructure security controls across LangSmith, LangGraph, and the LangChain SDK ecosystem (Python/TS/Go). - Secure-by-default authN/Z: Evolve SSO/SAML/OIDC/SCIM, token lifecycles, service‑to‑service auth, and tenant isolation for cloud and self‑hosted customers. - Ship code & reviews: Land secure designs, write PRs, and introduce lightweight checks (linters, dependency/supply‑chain scanning, SBOM/SLSA provenance). - Hardening & operations: Network segmentation/Zero Trust, Kubernetes posture, secrets management, key rotation, least‑privilege IAM, egress controls. - Incident readiness: Develop IR runbooks, detection rules, and tabletop exercises; lead post‑incident forensics and blameless RCAs. - Vuln management: Own scanning/triage/patch SLAs; coordinate with engineering to remediate quickly without slowing delivery.   How to be successful in this role - 5+ years in security engineering with strong software skills (Python or Go; TypeScript a plus). - Depth in cloud/Kubernetes security (e.g., GCP/AWS IAM, workload identity, admission controls, network policies). - Hands‑on AppSec: code review, threat modeling, secure design, secrets & key management, authn/z patterns, multi‑tenant isolation. - Experience building detection & response and running incident management. - Familiarity with supply‑chain security (SBOM, sigstore/cosign, SLSA‑style controls) and dependency risk management. - Clear, pragmatic communication with engineers and customers. Nice to have - Security for SaaS + self‑hosted offerings, including air‑gapped deployments. - Exposure to SOC 2 / ISO 27001 programs and evidence automation. - Experience with Go services and Infra as Code (Terraform/Helm), plus policy‑as‑code (OPA/Gatekeeper/Kyverno). - Knowledge of privacy patterns (data minimization, retention, masking, workspace scoping)..   Compensation & Benefits - We offer competitive compensation that includes base salary, meaningful equity, and benefits such as health and dental coverage, flexible vacation, a 401(k) plan, and life insurance. Actual compensation will vary based on role, level, and location. For team members in the EU and UK, we provide locally competitive benefits aligned with regional norms and regulations. - Annual salary range: $175,000-$215,000 USD for Senior Engineers COMPENSATION PHILOSOPHY: We offer competitive compensation that includes base salary, variable compensation for relevant roles, meaningful equity, benefits, and perks. We are hiring for this role across a range of scope, and the compensation band reflects that. Your offer depends on what we learn about your impact, craft, leadership, and communication during the interview process. We will tell you early where you’re tracking and talk comp with you before offer stage. Team members in the EU, UK, and APAC receive locally competitive benefits aligned with regional norms and regulations. BENEFITS Benefits include medical, dental, and vision coverage, flexible vacation, a 401(k) plan, meals on in-office days in the US and more.

Location: United States What the Role Is We’re hiring a Staff Software Engineer who’ll be the technical lead for a new Search team at Babylist. This new team will be focused on moving Babylist’s search capability in-house, making our search results more relevant and personalized, and partnering with other teams to transform search in the product from functional to delightful. Registry building is the heart of the Babylist product. Every parent builds a list of dozens of products, from swaddle to stroller, with real stakes (a friend or family member is going to buy these things, and a baby is going to use them). As a universal registry, this registry also typically spans many retailers. This makes registry building on Babylist one of the most interesting search and personalization problems in consumer e-commerce. This is zero-to-one work, with big ambitions. You’d build a search capability from the ground up, with a clear mandate from leadership to do it. In your first year, we expect to have rebuilt search to be highly relevant and personalized, and delivered big bets like universal search: one experience across items we sell and items from other retailers, and across products, guides and registries. We also want to work with Addie, our AI registry assistant, to have LLMs play a role in the search experience. If you want to tune an established search system, this isn’t the right role. If you’ve worked inside a strong search team and want to build a new system from scratch at a company earlier in that journey, read on. What You'll Own A Staff Engineer at Babylist sets direction. You’ll be the technical lead for search: you decide where it goes over the next year or two, sequence the bets that get there, and make the technical and product calls along the way. You’re still in the code and will drive initiatives forward across multiple teams. You’ll have software engineering and machine learning engineering support on the team. A few examples of initiatives you’d drive on the Search team: Build search in-house. Choose the architecture for indexing, retrieval and serving, and plan a seamless migration. The system you design replaces what we use today and is the foundation for everything below. Define what a good result is. Decide how we measure result quality, then improve it through query understanding, ranking and the behavioral signals we collect. Unify search across everything we show. One experience across items we sell and items from other retailers, and across products, guides and registries, including how to rank items we don’t sell next to ones we do. Decide how search and recommendations fit together. Bring what we know about a user and their registry into results, and decide how much retrieval and ranking search shares with our recommendation models. Make search conversational. Work with the Addie team, our AI registry assistant, on queries like “best stroller for urban family,” where the shopper describes a need instead of naming a product. Like every Staff Engineer here, you will: Take a fuzzy business problem from the first sketch through to production, and stay on the hook for whether it actually helped customers. Build the platform or tooling other teams adopt by choice, and own it as it scales. Set the standard for how search builds with AI. Decide what good looks like, build the patterns and evals that get agents there and catch the output that's confidently wrong before it ships. Partner with product, design and data as a peer, shaping what's worth building from the start. Coach Senior engineers through the hard calls, the ambiguous ones as much as the technical ones Who You Are You meet the Staff bar at Babylist. You’ve shipped production systems for enough years to have earned strong opinions, and you hold them loosely. You can pick up an ambiguous problem and start moving before anyone hands you the full picture. You’ve already changed how a team builds with AI, and the new way stuck. On top of that, you’ve worked on search problems before, ideally in an e-commerce setting. This role needs depth in the systems behind search and a clear focus on the people using it. You bring: Hands-on search experience: building and running search in production, including indexing, retrieval, ranking and query understanding. You know how to keep a search service fast, relevant and reliable as traffic and catalog size grow. Experience evaluating search quality: offline and online, including relevance judgments and A/B tests. A focus on the user: you use query data, behavior and judgment about result quality to decide what to build, and you can connect a search change to what shoppers do, and to conversion or revenue. Comfort building from zero: you can define the problem, design the system and own it end to end. You measure yourself by impact: a customer outcome, or a system a dozen teams come to depend on. Compensation We post real numbers. For a US-based Staff Engineer, the starting base salary range is $233,500 to $290,700, plus a target annual bonus of 20 percent of base. That's total target cash of roughly $280,200 to $348,840. On top of that you get meaningful equity and a 401(k) match. Where you start in that range depends on your experience, and your pay grows from there with performance and scope. How We Build AI is the default here. Engineers run agentic sessions for most of the work, and a lot of the interesting engineering now lives in the scaffolding that makes the agents good: the eval harnesses, the curated context, custom review skills and fast CI. Agents also triage incidents and handle a big share of support. A human always owns the outcome. The architecture is intentionally simple: one Rails monolith, MySQL and few moving parts. That's deliberate. Simple infrastructure lets us move fast and lets AI reason about the whole system, so the hardest problems are the ones in front of customers. The Stack Core app: Rails, Packwerk, React, TypeScript, Sidekiq Mobile: iOS (Swift), Android (Kotlin) Data, search & events: MySQL, Snowflake, dbt, Airflow, Weaviate, WarpStream, Hex, Sigma Machine learning: deep learning, matrix factorization, retrieval & ranking, AWS SageMaker, MLflow AI & dev tooling: Claude Code, Devin, Linear, CodeRabbit, Warden, LangChain, Bedrock AgentCore, Maxim Infra & ops: AWS, Kubernetes, Terraform, GitHub Actions, Datadog, incident.io, LaunchDarkly Key integrations: Shopify (payments), Iterable (CRM) Why Babylist An engineer, expecting her first baby, couldn't find the registry she wanted. So she built it. That's how Babylist started, and it's still how we work: engineers solving problems for families. Becoming a parent is one of the biggest moments in a person's life. Millions reach it for the first time every year, making thousands of decisions and figuring it out as they go. That's who we build for, and we're a long way from done. Ten million people give gifts through Babylist every year. We did more than $750M in revenue in 2025, up 45 percent over the year before, and we've been profitable for eight years while staying independent. So you can take on a hard, multi-year problem without watching over your shoulder for the next round or the next correction. And the team is small, around 65 engineers, so what you ship stays visible and your scope stays wide. How We Work Remote-first across the US and Canada, and we have been for years. That's not changing. We trust you to own your time and your outcomes, and we get everyone in a room together twice a year. Teams are small, pods of three to five engineers, so nothing you ship disappears into a committee. You'll work shoulder to shoulder with product, design and data, and with the partners across the business who rely on what you ship. You'll also stay close to customers yourself: sitting in on user interviews, watching session recordings, riding along with support. Here that's part of the engineering job, on a regular basis. How We Hire Three rounds, usually two to three weeks start to finish. Recruiter conversation (30 minutes). Trade context: what you want next, what we're building and straight answers on comp, team and remote. Technical screen (1 hour). One round, no AI, language-agnostic. Reach for Google when you normally would. We just want to see how you reason from first principles. Final round (4 hours). Four one-hour interviews: system design, AI-assisted coding with the tools you'd actually use here, product sense and culture and values. You'll hear back either way within 24 to 48 hours. Benefits Company-paid medical and fully covered dental and vision A 401(k) match Generous paid parental leave for birthing and non-birthing parents, plus a gradual return-to-work program Winter Wonder Week, a paid company-wide week off at the end of the year A remote-work stipend Mental-health and wellness support A Few Things to Know We record and transcribe interviews to evaluate candidates, in line with applicable privacy laws. We expect you to use AI in your work and we welcome it in the process, but what you submit and say should reflect your own thinking. If you have a family member or close relationship with a Babylist employee, let your recruiter know. Official outreach only ever comes from an @babylist.com address

Location: Bangalore, India GitLab is the intelligent orchestration platform for DevSecOps. GitLab enables organizations to increase developer productivity, improve operational efficiency, reduce security and compliance risk, and accelerate digital transformation. More than 50 million registered users and more than 50% of the Fortune 100* trust GitLab to ship better, more secure software faster. The same principles built into our products are reflected in how our team works: we embrace AI as a core productivity multiplier, with all team members expected to incorporate AI into their daily workflows to drive efficiency, innovation, and impact. GitLab is where careers accelerate, innovation flourishes, and every voice is valued. Our high-performance culture is driven by our values and continuous knowledge exchange, enabling our team members to reach their full potential while collaborating with industry leaders to solve complex problems. Co-create the future with us as we build technology that transforms how the world develops software. * Fortune 500® is a registered trademark of Fortune Media IP Limited, used under license. Claim based on GitLab data. Fortune 100 refers to the top 20% ranked companies in the 2025 Fortune 500 list, published in June 2025. Fortune and Fortune Media IP Limited are not affiliated with, and do not endorse products or services of GitLab. An overview of this role As a Staff Software Engineer on GitLab's Authentication team, you'll be the technical lead for how every user, application, token, and automated agent proves who it is on GitLab.com, Self-Managed, and Dedicated. We're an OAuth 2.0 and OpenID Connect (OIDC) provider for millions of users. We also accept identity from enterprise identity providers through Security Assertion Markup Language (SAML) and Lightweight Directory Access Protocol (LDAP). Behind that sit more than ten token types and several session methods that grew inside the Ruby on Rails monolith. You'll help us move this work onto GATE, GitLab's new authentication and authorization platform. GATE exchanges external credentials for short-lived tokens at the edge through a Secure Token Service written in Go.You'll set our technical direction, lead the hardest protocol and migration work yourself, and help senior engineers own the rest. Our projects include: OAuth across Cells: Making OAuth clients, grants, and tokens work when users and organizations live in different cells Global identities: One user identity that works across organizations and cells Token consolidation: Reducing the number of token types and moving token issuance and validation out of the monolith Identity for artificial intelligence (AI) agents and workloads: OAuth for Model Context Protocol (MCP) clients, workload identity federation, and service identities with narrow scopes What you'll do Own the technical direction for authentication protocols at GitLab, including OAuth 2.0, OIDC, SAML, and token exchange, across the Rails monolith and GATE services. Write and drive design documents and architecture decision records (ADRs) for multi-quarter work, then break that work into increments that ship behind feature flags. Lead migrations that must not break existing customers: token formats, client and grant state, and session behavior on GitLab.com, Self-Managed, and Dedicated. Be hands-on in the most complex parts of the code, in Ruby on Rails and Go, and review the designs and merge requests of other engineers. Agree interface contracts with the GATE Core, GATE Infra, Authorization, and Tenant Scale (Cells and Organizations) teams, and keep them current as the architecture changes. Treat authentication as a security and availability boundary: threat-model new flows, add rate limits and abuse controls, and lead the technical response to authentication incidents. Mentor senior and intermediate engineers on the team and raise the protocol and security knowledge of the whole group. Work asynchronously with engineers in India, Europe, and the Americas, with most decisions made in writing. Take part in on-call or operational support where your team’s ownership model requires it. What you'll bring Significant experience building and running production authentication or identity systems at scale, with technical leadership across teams that moves unclear problems to written decisions and shipped code. Deep, hands-on knowledge of OAuth 2.0 and OIDC: authorization code with Proof Key for Code Exchange (PKCE), refresh token rotation, token exchange, client registration, scopes, and JSON Web Tokens (JWTs) with signing keys and key rotation. Working knowledge of SAML 2.0 single sign-on and enterprise identity integration (LDAP, System for Cross-domain Identity Management (SCIM), or similar). Strong production experience in Ruby on Rails or Go, willingness to work in both, and experience with careful refactors, feature-flagged rollouts, and backward-compatible migrations in large, long-lived codebases. A security mindset. You think about token theft, replay, revocation, and blast radius before you think about elegance. Experience with multi-tenant software as a service (SaaS) systems, including data isolation and performance under high request volume. Clear, concise written English and comfort working in an all-remote, asynchronous company. Optional experience: authentication libraries (Doorkeeper, Devise, or OmniAuth), passkeys or multi-factor authentication, workload identity, gateways or policy engines, AI agent authentication, or multi-region architectures. Contributions to identity standards or open source identity projects are also welcome. About the team The Authentication group is part of the Security Platform stage, alongside Authorization, GATE Core, GATE Infra, Secrets Manager, and other security teams. We own sign-in, single sign-on, OAuth, and personal and service tokens. Our work touches every GitLab customer, and much of it is on the critical path for Cells and for GitLab's AI agent platform. How GitLab Supports Full-Time Employees Benefits to support your health, finances, and well-being Flexible Paid Time Off Team Member Resource Groups Equity Compensation & Employee Stock Purchase Plan Growth and Development Fund Parental Leave Please note that we welcome interest from candidates with varying levels of experience; many successful candidates do not meet every single requirement. Additionally, studies have shown that people from underrepresented groups are less likely to apply to a job unless they meet every single qualification. If you're excited about this role, please apply and allow our recruiters to assess your application. Country Hiring Guidelines: GitLab hires new team members in countries around the world. All of our roles are remote, however some roles may carry specific location-based eligibility requirements. Our Talent Acquisition team can help answer any questions about location after starting the recruiting process. Privacy Policy: Please review our Recruitment Privacy Policy. Your privacy is important to us. GitLab is proud to be an equal opportunity workplace and is an affirmative action employer. GitLab’s policies and practices relating to recruitment, employment, career development and advancement, promotion, and retirement are based solely on merit, regardless of race, color, religion, ancestry, sex (including pregnancy, lactation, sexual orientation, gender identity, or gender expression), national origin, age, citizenship, marital status, mental or physical disability, genetic information (including family medical history), discharge status from the military, protected veteran status (which includes disabled veterans, recently separated veterans, active duty wartime or campaign badge veterans, and Armed Forces service medal veterans), or any other basis protected by law. GitLab will not tolerate discrimination or harassment based on any of these characteristics. See also GitLab’s EEO Policy and EEO is the Law . If you have a disability or special need that requires accommodation , please let us know during the recruiting process .

Location: San Francisco, CA | New York City, NY About Anthropic Anthropic’s mission is to create reliable, interpretable, and steerable AI systems. We want AI to be safe and beneficial for our users and for society as a whole. Our team is a quickly growing group of committed researchers, engineers, policy experts, and business leaders working together to build beneficial AI systems. https://job-boards.greenhouse.io/anthropic/jobs/4669581008 About Anthropic Anthropic’s mission is to create reliable, interpretable, and steerable AI systems. We want AI to be safe and beneficial for our users and for society as a whole. Our team is a quickly growing group of committed researchers, engineers, policy experts, and business leaders working together to build beneficial AI systems. About the Team Our team is organized around the north star goal of building an AI scientist – a system capable of solving the long term reasoning challenges and basic capabilities necessary to push the scientific frontier. About the role As a Research Engineer on our team you will work end to end across the whole model stack, identifying and addressing key infra blockers on the path to scientific AGI. Strong candidates should have familiarity with elements of language model training, evaluation, and inference and eagerness to quickly dive and get up to speed in areas they are not yet an expert on. This may include performance optimization, distributed systems, VM/sandboxing/container deployment, and large scale data pipelines. Join us in our mission to develop advanced AI systems pushing the frontiers of science and benefiting humanity. Responsibilities: Design and implement large-scale infrastructure systems to support AI scientist training, evaluation, and deployment across distributed environments Identify and resolve infrastructure bottlenecks impeding progress toward scientific capabilities Develop robust and reliable evaluation frameworks for measuring progress towards scientific AGI. Build scalable and performant VM/sandboxing/container architectures to safely execute long-horizon AI tasks and scientific workflows Collaborate to translate experimental requirements into production-ready infrastructure Develop large scale data pipelines to handle advanced language model training requirements Optimize large scale training and inference pipelines for stable and efficient reinforcement learning You may be a good fit if you: Have 6+ years of highly-relevant experience in infrastructure engineering with demonstrated expertise in large-scale distributed systems Are a strong communicator and enjoy working collaboratively Possess deep knowledge of performance optimization techniques and system architectures for high-throughput ML workloads Have experience with containerization technologies (Docker, Kubernetes) and orchestration at scale Have proven track record of building large-scale data pipelines and distributed storage systems Excel at diagnosing and resolving complex infrastructure challenges in production environments Can work effectively across the full ML stack from data pipelines to performance optimization Have experience collaborating with other researchers to scale experimental ideas Thrive in fast-paced environments and can rapidly iterate from experimentation to production Strong candidates may also have: Experience with language model training infrastructure and distributed ML frameworks (PyTorch, JAX, etc.) Background in building infrastructure for AI research labs or large-scale ML organizations Knowledge of GPU/TPU architectures and  language model inference optimization Experience with cloud platforms (AWS, GCP) at enterprise scale Familiarity with VM and container orchestration. Experience with workflow orchestration tools and experiment management systems History working with large scale reinforcement learning Comfort with large scale data pipelines (Beam, Spark, Dask, …) The annual compensation range for this role is listed below. For sales roles, the range provided is the role’s On Target Earnings ("OTE") range, meaning that the range includes both the sales commissions/sales bonuses target and annual base salary for the role. Annual Salary: $350,000 — $850,000 USD Logistics Minimum education: Bachelor’s degree or an equivalent combination of education, training, and/or experience Required field of study: A field relevant to the role as demonstrated through coursework, training, or professional experience Minimum years of experience: Years of experience required will correlate with the internal job level requirements for the position Location-based hybrid policy: Currently, we expect all staff to be in one of our offices at least 25% of the time. However, some roles may require more time in our offices. Visa sponsorship: We do sponsor visas! However, we aren't able to successfully sponsor visas for every role and every candidate. But if we make you an offer, we will make every reasonable effort to get you a visa, and we retain an immigration lawyer to help with this. For roles performing work in California: We will consider for employment all qualified applicants, including those with arrest or conviction records or criminal histories, in a manner consistent with the requirements of applicable state and local laws, including the San Francisco Fair Chance Ordinance, the City of Los Angeles’ Fair Chance Initiative for Hiring Ordinance, the Los Angeles County Fair Chance Ordinance for Employers, and the California Fair Chance Act. We encourage you to apply even if you do not believe you meet every single qualification. Not all strong candidates will meet every single qualification as listed.  Research shows that people who identify as being from underrepresented groups are more prone to experiencing imposter syndrome and doubting the strength of their candidacy, so we urge you not to exclude yourself prematurely and to submit an application if you're interested in this work. We think AI systems like the ones we're building have enormous social and ethical implications. We think this makes representation even more important, and we strive to include a range of diverse perspectives on our team. Your safety matters to us. To protect yourself from potential scams, remember that Anthropic recruiters only contact you from @anthropic.com email addresses. In some cases, we may partner with vetted recruiting agencies who will identify themselves as working on behalf of Anthropic. Be cautious of emails from other domains. Legitimate Anthropic recruiters will never ask for money, fees, or banking information before your first day. If you're ever unsure about a communication, don't click any links—visit anthropic.com/careers directly for confirmed position openings. How we're different We believe that the highest-impact AI research will be big science. At Anthropic we work as a single cohesive team on just a few large-scale research efforts. And we value impact — advancing our long-term goals of steerable, trustworthy AI — rather than work on smaller and more specific puzzles. We view AI research as an empirical science, which has as much in common with physics and biology as with traditional efforts in computer science. We're an extremely collaborative group, and we host frequent research discussions to ensure that we are pursuing the highest-impact work at any given time. As such, we greatly value communication skills. The easiest way to understand our research directions is to read our recent research. This research continues many of the directions our team worked on prior to Anthropic, including: GPT-3, Circuit-Based Interpretability, Multimodal Neurons, Scaling Laws, AI & Compute, Concrete Problems in AI Safety, and Learning from Human Preferences. Come work with us! Anthropic is a public benefit corporation headquartered in San Francisco. We offer competitive compensation and benefits, optional equity donation matching, generous vacation and parental leave, flexible working hours, and a lovely office space in which to collaborate with colleagues. Guidance on Candidates' AI Usage: Learn about our policy for using AI in our application process.

Location: Bangalore, India GitLab is the intelligent orchestration platform for DevSecOps. GitLab enables organizations to increase developer productivity, improve operational efficiency, reduce security and compliance risk, and accelerate digital transformation. More than 50 million registered users and more than 50% of the Fortune 100* trust GitLab to ship better, more secure software faster. The same principles built into our products are reflected in how our team works: we embrace AI as a core productivity multiplier, with all team members expected to incorporate AI into their daily workflows to drive efficiency, innovation, and impact. GitLab is where careers accelerate, innovation flourishes, and every voice is valued. Our high-performance culture is driven by our values and continuous knowledge exchange, enabling our team members to reach their full potential while collaborating with industry leaders to solve complex problems. Co-create the future with us as we build technology that transforms how the world develops software. * Fortune 500® is a registered trademark of Fortune Media IP Limited, used under license. Claim based on GitLab data. Fortune 100 refers to the top 20% ranked companies in the 2025 Fortune 500 list, published in June 2025. Fortune and Fortune Media IP Limited are not affiliated with, and do not endorse products or services of GitLab. An overview of this role As an Engineering Manager on GitLab's Authentication team, you'll build and lead a team of engineers in Bangalore who own how users, applications, tokens, and artificial intelligence (AI) agents sign in to GitLab. At GitLab, we see our team as our product. You'll need to be technically credible in authentication protocols such as OAuth 2.0, OpenID Connect (OIDC), and Security Assertion Markup Language (SAML). Your main focus will be hiring well, growing your engineers, and helping the team deliver on commitments that other parts of GitLab depend on. You'll guide your team's work on OAuth across Cells, global identities, token consolidation, and moving authentication out of the Ruby on Rails monolith onto GATE, GitLab's new authentication and authorization platform. You'll manage these multi-quarter programs with fixed deadlines and many dependent teams. What you'll do Hire, onboard, and grow a team of senior and intermediate engineers in Bangalore, working with talent acquisition during a high-volume hiring period. Own delivery for your team's part of the authentication roadmap: plan in milestones, track risks early, and communicate clearly when a date is at risk and what would change it. Work with your Staff Engineer and Product Manager to turn architecture decisions into shippable, feature-flagged increments. Protect your team's focus. Manage requests from other teams through one intake path, and make trade-offs visible to leadership. Coordinate with the GATE Core, GATE Infra, Authorization, and Tenant Scale (Cells and Organizations) teams across India, Europe, and the Americas. Make sure the team runs what it builds, including on-call coverage, incident response for authentication issues, rate limits, and production readiness for new services. Build a team culture where security comes first, decisions are written down, and code review is thorough and kind. Give clear, regular feedback, run career conversations, and handle performance issues early. Take part in on-call or operational support where your team’s ownership model requires it. What you'll bring Significant experience as an engineering manager of backend or platform teams, including hiring and growing senior engineers. A background as an engineer in identity, authentication, or security systems, with enough depth in OAuth 2.0, OIDC, SAML, and JSON Web Tokens to review designs and ask probing questions. Experience delivering complex, multi-team programs against fixed dates, including migrations that must not disrupt existing customers. Experience running production services, including on-call coverage, incident response, and reliability work. Working knowledge of Ruby on Rails or Go. Experience building a new team or growing one quickly while keeping the hiring bar high. Clear, concise written English and comfort leading a team in an all-remote, asynchronous company across time zones. About the team We're part of the Security Platform stage, alongside Authorization, GATE Core, GATE Infra, Secrets Manager, and other security teams. You'll partner closely with our existing Authentication engineers in Europe and the Americas. How GitLab Supports Full-Time Employees Benefits to support your health, finances, and well-being Flexible Paid Time Off Team Member Resource Groups Equity Compensation & Employee Stock Purchase Plan Growth and Development Fund Parental Leave Please note that we welcome interest from candidates with varying levels of experience; many successful candidates do not meet every single requirement. Additionally, studies have shown that people from underrepresented groups are less likely to apply to a job unless they meet every single qualification. If you're excited about this role, please apply and allow our recruiters to assess your application. Country Hiring Guidelines: GitLab hires new team members in countries around the world. All of our roles are remote, however some roles may carry specific location-based eligibility requirements. Our Talent Acquisition team can help answer any questions about location after starting the recruiting process. Privacy Policy: Please review our Recruitment Privacy Policy. Your privacy is important to us. GitLab is proud to be an equal opportunity workplace and is an affirmative action employer. GitLab’s policies and practices relating to recruitment, employment, career development and advancement, promotion, and retirement are based solely on merit, regardless of race, color, religion, ancestry, sex (including pregnancy, lactation, sexual orientation, gender identity, or gender expression), national origin, age, citizenship, marital status, mental or physical disability, genetic information (including family medical history), discharge status from the military, protected veteran status (which includes disabled veterans, recently separated veterans, active duty wartime or campaign badge veterans, and Armed Forces service medal veterans), or any other basis protected by law. GitLab will not tolerate discrimination or harassment based on any of these characteristics. See also GitLab’s EEO Policy and EEO is the Law . If you have a disability or special need that requires accommodation , please let us know during the recruiting process .

Location: Москва Мы исследовательское подразделение, которое отвечает в Сбере за «человека»: поведение, психология, тренды, изменения, особенности восприятия и мышления. Наша ключевая задача: исследовать и рассказывать, что происходит и будет происходить с человеком, как эти изменения переложить в продукт, коммуникации, HR-процессы, чтобы они стали лучше.  Работа в Лаборатории – это возможность прикоснуться практически к любому бизнес-подразделению экосистемы Сбера, к большим данным Сбера, к клиентской базе более 100 млн человек. Мы ищем ML Developer'а — человека, который будет реализовывать ML-проекты Лаборатории: обучение и дообучение моделей, построение data-пайплайнов, вывод решений в production. В отличие от LLM-генералистов команды, которые быстро вайбкодят прототипы, вы будете отвечать за то, чтобы модели работали надёжно, воспроизводимо и масштабируемо, а также дорабатывать прототипы до продакшен решений. Вы рамках Лаборатории вы будете в команде “больших данных и гиперперсонализации”, всего около 25 человек, из которых DS-команда – порядка 10 человек, остальные – исследователи, психологи и социологи. Лаборатория работает на стыке поведенческой науки, данных и AI. Мы строим модели, которые описывают и предсказывают поведение человека — от сегментации клиентов до цифровых двойников. ML Developer — это тот, кто превращает исследовательские гипотезы в работающие модели и надёжную инфраструктуру вокруг них. Наш технологический стек: * ML/DL: HuggingFace Transformers, SentenceTransformers, CatBoost, XGBoost * LLM & Vector Search: локальные модели (Llama 3, Qwen), API (OpenAI), FAISS * Data & Infra: ClickHouse, Docker, FastAPI, Postgres, RabbitMQ, MongoDB * MLOps & Orchestration: MLflow, W&B, Prefect, Dagster, Ray, Prometheus, OpenTelemetry * Инструменты: Hydra, Poetry. Первый этап отбора на эту вакансию – общение с AI-рекрутером. После отклика ждите сообщение от него в мессенджере, диалог займёт примерно 10 минут. Задача AI-рекрутера — уточнить недостающие детали и ускорить рассмотрение вашей кандидатуры. AI-рекрутер только начинает свой путь, поэтому просим относиться с пониманием. Ваш опыт и участие помогут сделать его удобным и полезным для всех! Обязанности ### Чем предстоит заниматься: * классический ML/NLP: разработка и обучение моделей для анализа текстов, поведенческих данных, психометрических профилей. * кластеризация, классификация, NER, sentiment — всё, что требуется для исследований на данных. * LLM-интеграция: fine-tuning и дообучение языковых моделей под задачи Лаборатории, построение embedding-пайплайнов, проектирование RAG-архитектур с графовыми и векторными БД. * работа с промптами на уровне системной архитектуры, а не ad hoc экспериментов * Production & MLOps: вывод моделей из ноутбуков в production — контейнеризация, CI/CD, мониторинг, версионирование моделей и данных * построение воспроизводимых пайплайнов обработки данных. Требования ### Наши ожидания от кандидата: ML & NLP: * практический опыт обучения и дообучения моделей (классический ML + deep learning) * уверенная работа с NLP: text classification, NER, embeddings, topic modeling, умение работать с ASR, Text-to-Speech задачами * опыт fine-tuning трансформерных моделей (HuggingFace Transformers, PEFT, LoRA, QLoRA) * понимание метрик качества моделей и умение проектировать эксперименты. LLM & Retrieval-архитектуры: * опыт работы с LLM API (OpenAI, Anthropic и др.) на уровне production-решений * построение RAG-пайплайнов: chunking-стратегии, embedding-модели, векторные и графовые БД * понимание различий между векторным поиском и BM25 * работа с LangChain / LangGraph или аналогами. Engineering & MLOps: * Python как основной язык, уверенное владение * Docker, CI/CD, базовый DevOps * опыт работы с ML-оркестраторами (MLflow, W&B, Prefect, Dagster — хотя бы один) * навыки разработки API (FastAPI / аналоги) * работа с SQL-базами (ClickHouse, Postgres) и очередями (RabbitMQ и т.п.). Софты и майндсет: * техническое образование (ML, CS, математика, инженерия) * умение переводить абстрактные исследовательские гипотезы в формализованные ML-задачи * способность объяснять технические решения нетехнической аудитории (исследователям, бизнесу) * интерес к поведенческой науке, психологии — или хотя бы готовность погружаться. Будет плюсом: * опыт работы с распределёнными вычислениями (Ray, DeepSpeed) * знакомство с Prometheus, OpenTelemetry, observability-стеком * опыт работы с графовыми базами данных (Neo4j и т.п.) * контроль стоимости и latency AI-вызовов * публикации, pet-проекты, вклад в open-source * опыт использования GigaChat, Kandinsky и аналогов в продуктах, навыки создания и использования AI-агентов. Условия ### Мы предлагаем: * комфортный современный офис г. Москва, рядом с метро Кутузовская * офисный формат работы: 5/2 с 10 до 19, возможен гибрид (1-2 дня в неделю вне офиса) * ежегодный пересмотр зарплаты и годовая премия * корпоративный спортзал и зоны отдыха * более 400 образовательных программ СберУниверситета для профессионального и карьерного развития * расширенный ДМС, льготное страхование для семьи и корпоративная пенсионная программа * гибкий дисконт по ипотечному кредиту, равный 1/3 ключевой ставки ЦБ * подписка Прайм с возможностью совместного использования на трёх близких * вознаграждение за рекомендацию друзей в команду Сбера.

Location: Toronto; San Francisco; Canada; United States; New York Workplace: Remote WHO ARE WE? Cohere is the leading security-first enterprise AI company. We build cutting-edge foundation AI models and end-to-end products that are designed to solve real-world business problems. We’re training and deploying frontier models for enterprises who are building AI systems. We believe that our work is instrumental to the widespread adoption of AI and we are looking for folks that want to be part of that. We obsess over what we build. Each one of us is responsible for contributing to increasing the capabilities of our models and the value they drive for our customers. Cohere is a team of researchers, engineers, designers, and more, who are all passionate about their craft. We are a global technology company headquartered in Toronto with key offices in London, New York City, San Francisco, Montreal, Paris, Berlin and Seoul. Join us! Why this role? Cohere seeks an experienced and passionate VP of Services, Customer Success & Support to lead our Customer Success and Support teams. In this role, you will be responsible for leading and developing a talented post-sales Customer Success and Support team, who are the primary owners of business and technical relationships with our customers. The ideal candidate will have a background leading post-sales teams in the ML/AI/Data Engineering industry. In this role you will: - Drive product adoption and customer experience for our largest customers. - Define the customer lifecycle including roles and responsibilities, handoffs, and workflow & influence the product roadmap. - Lead customer-facing teams, including coaching sub-function leaders (Customer Success, Services, Technical Support) to ensure that all teams are aligned and focused on delivering an exceptional customer experience. - Measure and analyze customer data to identify trends and areas for improvement, including analyzing customer feedback, support metrics, and other customer-related data to develop insights that can be used to improve the customer experience, troubleshoot issues and showcase Cohere’s value proposition. - Enhance the customer journey, identifying the key touch points across interactions with customers and developing strategies to optimize each step of the journey. - Champion the customer within the organization, advocating for the customer in all decision-making processes at the executive level to ensure that customer needs are taken into account when making strategic decisions. - Collaborate and partner with other internal teams (pre-sales, Product Management, Machine Learning Engineerings) to ensure that the voice of the customer is represented in strategic decisions. You may be a good fit, if you have: - 10+ years of leadership experience developing top-performing teams in Customer Success, Technical Support and Services. - Work experience in the AI, ML, Data Engineering, Cloud, SaaS or Tech Infra industry. - Proven experiences being highly focussed on customer needs, you're an amazing relationship builder and you can proactively spot opportunities to engage customers and build loyalty. - An exceptional collaborative mindset and know how to engage and communicate with leadership and technical & non-technical stakeholders. - Strong experience building strategy, a willingness to execute and build from the ground up & are always willing to dig deeper and jump in when it comes to our customers. FULL-TIME EMPLOYEES AT COHERE ENJOY THESE PERKS: - A weekly lunch stipend of $75/£75 or equivalent in your local currency for lunch. - Full health and dental benefits, including a separate budget for mental health. - RRSP matching, 401K, Pension Scheme. - 100% Parental Leave top-up for up to 6 months, for either parent. - Annual enrichment benefits: Arts & culture, fitness/wellness, quality time, and a workspace improvement credit. Education & learning stipend for conferences, courses, and coaching. - 6 weeks of paid vacation (30 working days!) - Budget for traveling to other offices if you are remote, plus an annual company offsite. HOW AND WHERE WE WORK: - Cohere is remote-friendly, but we also have offices in Toronto, London, New York City, San Francisco, Montreal, Paris, Berlin and Seoul with more opening soon. - For those in the office: a daily lunch program, plenty of snacks, and regular community and social events. - For those not near an office: a co-working benefit so you can work alongside others in your city. - Everyone receives a $500 home office stipend to set up your workspace properly. If any of the above doesn’t line up exactly with your experience, we still encourage you to apply. We strive to create an inclusive work environment for all; we welcome applicants from all backgrounds and are committed to providing equal opportunities. Should you require any accommodations during the recruitment process, please submit an Accommodations Request Form https://docs.google.com/forms/d/12a6IrLdF3kI2nonKSr4tiFuz18rLQbaeYV-JM9L4o9Q/edit, and we will work together to meet your needs. We may use AI-enabled tools to screen and assess applicants against the criteria for this position. This helps our recruiters identify potentially qualified candidates, but it doesn't limit the applications our recruiters may review or consider. Beware of Scams: Cohere will never ask for payment or third-party services (e.g., CV writing) as part of our hiring process. All legitimate roles are listed on the Cohere careers page and LinkedIn only, with all communications from Cohere employees coming from an @cohere.com or @cw.cohere email alias. If jobs are viewed on other sites then please verify these through our official careers https://cohere.com/careers page.

Location: Москва Привет! Это GigaChat Vision — команда, которая делает полный цикл обучения VLM-моделей. Ищем сильного senior-инженера в RL-направление: человека, который будет драйвить ключевые исследования и разработки, влиять на качество моделей и доводить сложные идеи до работающих решений. _Первый этап отбора на эту вакансию – общение с AI-рекрутером. После отклика вам на почту и в чат на платформе HeadHunter придет приглашение пройти первичное интервью с ГигаРекрутером в Telegram. Диалог займёт примерно 10 минут. Его задача — уточнить недостающие детали и ускорить рассмотрение вашей кандидатуры. ГигаРекрутер только начинает свой путь, поэтому просим относиться с пониманием. Ваш опыт и участие помогут сделать его удобным и полезным!_ Обязанности * Разрабатывать и улучшать RL-подходы для обучения VLM/LLM-моделей: выбор алгоритмов, постановка экспериментов и анализ результатов; * Проектировать reward-функции и пайплайны обучения, выстраивать стратегии масштабирования под разные домены и сценарии, напрямую влияя на метрики качества; * Определять требования к данным для RL: участвовать в построении пайплайнов сбора, фильтрации и подготовки датасетов; * Развивать систему оценки reasoning-качества: внедрять и улучшать метрики в существующем eval-фреймворке, предлагать новые способы измерения качества под новые возможности модели; * Работать на стыке с Pretrain / SFT / Infra: обеспечивать согласованность решений и перенос экспериментов в продакшн-пайплайн; * Следить за состоянием области и переводить свежие идеи из статей в проверяемые эксперименты и инженерные решения; * Глубоко погружаться в технические задачи: дебажить эксперименты, разбирать аномалии в обучении, находить узкие места и доводить решения до результата; * Делиться экспертизой с командой: участвовать в код-ревью, помогать улучшать качество решений и подходы к экспериментам; * Развивать инфраструктуру для RL. Требования * Глубокое понимание RL для LLM/VLM (RLHF, GRPO, PPO) и практический опыт; * Понимание полного цикла обучения VLM/LLM (pretrain → SFT → RL) и того, как решения на каждом этапе влияют на финальные метрики; * Опыт с распределённым обучением (DeepSpeed, FSDP) и inference-фреймворками. * Сильный практический опыт постановки, проведения и анализа RL-экспериментов; * Умение работать в условиях неопределённости и самостоятельно двигать сложные технические задачи; * Умение выстраивать процессы в условиях неопределённости; * Опыт взаимодействия со смежными командами и стейкхолдерами; * Системное мышление: способность видеть картину целиком — от данных и reward-дизайна до eval и продакшн-метрик. **Будет плюсом:** * Опыт менторинга или технического лидерства в рамках отдельных проектов; * Публикации или open-source вклад в области RL/LLM/VLM; * Опыт вывода RL-обученных моделей в продакшн и поддержки их качества. Условия * крупнейшее DS&AI community — более 600 DS-специалистов банка * дайджест о самых последних разработках в области DS&AI и отчеты с крупнейших конференций мира * возможность быть соавтором НИРов и статей для международных конференций * возможность выбрать удобный формат работы: гибрид или офис * ежегодный пересмотр зарплаты, годовая премия * корпоративный спортзал и зоны отдыха * более 400 образовательных программ СберУниверситета для профессионального и карьерного развития * расширенный ДМС, льготное страхование для семьи и корпоративная пенсионная программа * ипотека выгоднее до 7% для каждого сотрудника * бесплатная подписка СберПрайм+, скидки на продукты компаний-партнеров * вознаграждение за рекомендацию друзей в команду Сбера.

Location: Москва Привет! Это GigaChat Vision — команда, которая делает полный цикл обучения VLM-моделей. Ищем сильного инженера в RL-направление: человека, который будет драйвить ключевые исследования и разработки, влиять на качество моделей и доводить сложные идеи до работающих решений. _Первый этап отбора на эту вакансию – общение с AI-рекрутером. После отклика вам на почту и в чат на платформе HeadHunter придет приглашение пройти первичное интервью с ГигаРекрутером в Telegram. Диалог займёт примерно 10 минут. Его задача — уточнить недостающие детали и ускорить рассмотрение вашей кандидатуры. ГигаРекрутер только начинает свой путь, поэтому просим относиться с пониманием. Ваш опыт и участие помогут сделать его удобным и полезным!_ Обязанности * Разрабатывать и улучшать RL-подходы для обучения VLM/LLM-моделей: выбор алгоритмов, постановка экспериментов и анализ результатов; * Проектировать reward-функции и пайплайны обучения, выстраивать стратегии масштабирования под разные домены и сценарии, напрямую влияя на метрики качества; * Определять требования к данным для RL: участвовать в построении пайплайнов сбора, фильтрации и подготовки датасетов; * Развивать систему оценки reasoning-качества: внедрять и улучшать метрики в существующем eval-фреймворке, предлагать новые способы измерения качества под новые возможности модели; * Работать на стыке с Pretrain / SFT / Infra: обеспечивать согласованность решений и перенос экспериментов в продакшн-пайплайн; * Следить за состоянием области и переводить свежие идеи из статей в проверяемые эксперименты и инженерные решения; * Глубоко погружаться в технические задачи: дебажить эксперименты, разбирать аномалии в обучении, находить узкие места и доводить решения до результата; * Делиться экспертизой с командой: участвовать в код-ревью, помогать улучшать качество решений и подходы к экспериментам; * Развивать инфраструктуру для RL. Требования * Глубокое понимание RL для LLM/VLM (RLHF, GRPO, PPO) и практический опыт; * Понимание полного цикла обучения VLM/LLM (pretrain → SFT → RL) и того, как решения на каждом этапе влияют на финальные метрики; * Опыт с распределённым обучением (DeepSpeed, FSDP) и inference-фреймворками. * Сильный практический опыт постановки, проведения и анализа RL-экспериментов; * Умение работать в условиях неопределённости и самостоятельно двигать сложные технические задачи; * Умение выстраивать процессы в условиях неопределённости; * Опыт взаимодействия со смежными командами и стейкхолдерами; * Системное мышление: способность видеть картину целиком — от данных и reward-дизайна до eval и продакшн-метрик. **Будет плюсом:** * Опыт менторинга или технического лидерства в рамках отдельных проектов; * Публикации или open-source вклад в области RL/LLM/VLM; * Опыт вывода RL-обученных моделей в продакшн и поддержки их качества. Условия * крупнейшее DS&AI community — более 600 DS-специалистов банка * дайджест о самых последних разработках в области DS&AI и отчеты с крупнейших конференций мира * возможность быть соавтором НИРов и статей для международных конференций * возможность выбрать удобный формат работы: гибрид или офис * ежегодный пересмотр зарплаты, годовая премия * корпоративный спортзал и зоны отдыха * более 400 образовательных программ СберУниверситета для профессионального и карьерного развития * расширенный ДМС, льготное страхование для семьи и корпоративная пенсионная программа * ипотека выгоднее до 7% для каждого сотрудника * бесплатная подписка СберПрайм+, скидки на продукты компаний-партнеров * вознаграждение за рекомендацию друзей в команду Сбера.

Location: Hybrid - New York City About Vercel: Vercel is the agentic infrastructure company, freeing people and agents to ship what's next. For more than a decade we've helped builders move from idea to production with speed, security, and exceptional developer experience. Now we're scaling our products for both agents and people to ship and run software, built in the open and trusted by OpenAI, PayPal, Ramp, Supreme, and millions of developers worldwide. About the Role: We're looking for a leader to build and grow the Startups Activations team. This team is the voice of Vercel to the earliest-stage startups, helping founders make great infrastructure decisions from day one and land on Vercel before they ever need to migrate. You'll hire and develop up-and-coming talent from a wide range of backgrounds, including former founders, developers, and early-career sellers, and turn them into trusted advisors for technical founders. You'll build the machine: a high-velocity, repeatable motion that uses AI, automation, and product signals to reach thousands of early startups at the right moment. You'll set the operating rhythm, coach relentlessly, and turn what works into playbooks the rest of the Startups org can scale. If you’re based within a pre-determined commuting distance of one of our offices (SF, NY, London, or Berlin), the role includes in-office anchor days on Monday, Tuesday, and Friday. If you're located beyond that distance, the role is fully remote. For location-specific details, please connect with our recruiting team. What You Will Do: Hire & Grow Talent : Recruit, onboard, and ramp a diverse team of former founders, developers, and early-career sellers. Build a bench that grows into future AEs and leaders across Vercel. Coach & Develop : Run 1:1s, call reviews, and skill-building sessions that turn new sellers into credible, technical advisors for founders. Build the Activation Machine : Design a high-velocity motion that combines outbound, product-led signals, community, and ecosystem partners (accelerators, VCs, AI tooling) to get early startups deployed on Vercel. Be the Voice of Vercel to Startups : Make sure every early founder understands how Vercel's platform and Agent Stack help them build, ship, and scale, and why starting here is the right infra decision. AI, Automation & Playbooks : Use AI and automation to multiply team impact, spot patterns in usage and signups, and turn winning plays into repeatable motions. Pipeline & Execution : Own activation, conversion, and bookings targets. Define the leading indicators that predict long-term customer growth and hold the team to them. Forecasting & Performance Management : Inspect pipeline health, own forecast accuracy, and manage performance against activity, conversion, and quota. Cross-Functional Collaboration : Partner with Marketing, Product, Growth, RevOps, Solutions Engineering, and the Greenfield and Install Base teams to make handoffs seamless as startups scale. About You: 5+ years of experience in SaaS sales or startup go-to-market, including 2+ years managing quota-carrying sellers. A track record of hiring and developing early-career talent and people from non-traditional backgrounds into top performers. Experience building or running a high-velocity sales motion with volume, speed, and discipline. Comfort talking with technical founders about architecture and tradeoffs. You don't need to be an engineer, but you need credibility with engineers. Real curiosity about AI-native development and the tools shaping how startups build and ship. A builder's mindset: you like creating systems, not just running them, and you balance strategy with hands-on execution. A growth mindset, resilience, and strong shared ownership. Bonus If You: Have been a founder, early-stage operator, or developer. Have built a team or motion from zero inside a high-growth company. Have experience with startup ecosystems: accelerators, VCs, founder communities, or developer communities. Have sold developer tools, infrastructure, or AI products to startups. Compensation & Benefits: Competitive compensation package, including equity. Inclusive Healthcare Package. Learn and Grow - we provide mentorship and send you to events that help you build your network and skills. Flexible Time Off. We will provide you the gear you need to do your role, and a WFH budget for you to outfit your space as needed. The San Francisco, CA base pay range for this role is $280,000 - $330,000. Actual salary will be based on job-related skills, experience, and location. Compensation outside of San Francisco may be adjusted based on employee location. The total compensation package may include benefits, equity-based compensation, and eligibility for a company bonus or variable pay program depending on the role. Your recruiter can share more details during the hiring process. Disclosures: Privacy: Please review our Job Applicant Privacy Policy for more information on how we handle your data. Equal Opportunity: Vercel is committed to fostering and empowering an inclusive community within our organization. We do not discriminate on the basis of race, religion, color, gender expression or identity, sexual orientation, national origin, citizenship, age, marital status, veteran status, disability status, or any other characteristic protected by law. Vercel encourages everyone to apply for our available positions, even if they don't necessarily check every box on the job description.

Location: Canada What The Role Is We're hiring a Staff MLE for the Discovery team to own recommendations and personalization across Babylist's consumer experience — the homepage feed, product recs, search, and the ML-powered systems that make registry building feel effortless. Babylist was built on editorial recommendations: products chosen by people with deep baby gear expertise. That editorial foundation is a big part of why millions of families trust us. We're now building ML-powered personalization on top of it, using one of the richest first-party datasets in parenting. We're early in this work, and we have a real mandate. A small Discovery team has initial retrieval and reranking models live on parts of the site and a steady cadence of A/B tests. We are looking for a Staff MLE who has seen personalization done well at scale and can set the technical direction for where we go next. Registry building is the heart of the Babylist product. Every parent builds a list of dozens of products, from swaddle to stroller, with real stakes (a friend or family member is going to buy these things, and a baby is going to use them). As a universal registry, this registry also typically spans many retailers. This makes registry building on Babylist one of the most interesting personalization problems in consumer e-commerce: latent intent, life-stage progression, multi-stakeholder gift dynamics, deep declarative signal in millions of completed registries, cross-retailer datapoints, and a user who genuinely wants help. If you want to join a mature ML org and tune models at the margins, this isn't the right role. If you've worked inside a strong recommendations team, learned what good looks like, and want to build from zero-to-one at a company earlier in the journey, read on. What You'll Own You'll be the technical lead for personalization on the Discovery team, working alongside a PM, Engineering Manager, Senior MLE and fullstack software engineers. You set where our models go over the next year or two, sequence the bets that get there, and stay deep in building. A few examples of the problems you’ll get to shape: Product recommendations across every surface. Product detail pages, add-next recommendations after someone adds to their registry, vibes/aesthetic representations of products and the shared models underneath them. Personalizing the Feed. Deciding which products, editorial content and social content each family sees, and in what order. Personalizing search. Bringing what we know about a family into search results, and helping decide how much search should share retrieval and ranking with recommendations. Making Addie (our AI registry assistant) better at recs. Working with the team behind Addie so its product suggestions draw on the same models and signals as the rest of the site. In practice, you will: Take a fuzzy business problem from first sketch to production model, and own whether it moved the metric. Make the modeling and architecture calls that span surfaces and are expensive to reverse. Design the evaluation, offline and online, that tells us a model is good before and after it ships. Know when a rule or a simple baseline is the right first step, and when it's time to pursue more advanced approaches. Partner with product, design and data to help shape what's worth building. Coach engineers on the team, and more broadly be an ML expert and resource to the wider engineering org. Who You Are You’ve shipped recommendation and/or personalization systems that reached real users at scale within a consumer product, and can point to the business impact of your work. You’ve done this within a team that did ML well — you know what good looks like, and are motivated to bring that to a company earlier in their ML journey. You bring: Expertise in recommender fundamentals: for example, candidate generation vs. ranking, offline vs. online evaluation, cold start solutions, and handling position and popularity bias. Fluency in the Python ML stack and comfort owning models through deployment and monitoring, including contributions to our application backend. An ability to take an ambiguous problem and start moving before anyone hands you the full picture. A builder's instinct for early-stage ML. You understand effort vs. effectiveness tradeoffs and can advocate for when a rule beats a model, or a model is “good enough” for early learnings. An outcome and user focused mindset. You measure yourself by business impact, and you can connect a model change to user experience & registry adds, conversion or revenue. AI-native daily practice. You're already using AI to move faster and improve your output, and you stay curious about what's coming next. Compensation We post real numbers. For a Canada-based Staff Engineer, the starting base salary range is 299,300 to 372,600 CAD plus a target annual bonus of 20 percent of base. That's total target cash of roughly 359,160 to 447,120 CAD. On top of that you get meaningful equity and a 401(k) match. Where you start in that range depends on your experience, and your pay grows from there with performance and scope. How We Build AI is the default here. Engineers run agentic sessions for most of the work, and a lot of the interesting engineering now lives in the scaffolding that makes the agents good: the eval harnesses, the curated context, custom review skills and fast CI. Agents also triage incidents and handle a big share of support. A human always owns the outcome. The architecture is intentionally simple: one Rails monolith, MySQL and few moving parts. That's deliberate. Simple infrastructure lets us move fast and lets AI reason about the whole system, so the hardest problems are the ones in front of customers. The Stack Core app: Rails, Packwerk, React, TypeScript, Sidekiq Mobile: iOS (Swift), Android (Kotlin) Data, search & events: MySQL, Snowflake, dbt, Airflow, Weaviate, WarpStream, Hex, Sigma Machine learning: deep learning, matrix factorization, retrieval & ranking, AWS SageMaker, MLflow AI & dev tooling: Claude Code, Devin, Linear, CodeRabbit, Warden, LangChain, Bedrock AgentCore, Maxim Infra & ops: AWS, Kubernetes, Terraform, GitHub Actions, Datadog, incident.io, LaunchDarkly Key integrations: Shopify (payments), Iterable (CRM) Why Babylist An engineer, expecting her first baby, couldn't find the registry she wanted. So she built it. That's how Babylist started, and it's still how we work: engineers solving problems for families. Becoming a parent is one of the biggest moments in a person's life. Millions reach it for the first time every year, making thousands of decisions and figuring it out as they go. That's who we build for, and we're a long way from done. Ten million people give gifts through Babylist every year. We did more than $750M in revenue in 2025, up 45 percent over the year before, and we've been profitable for eight years while staying independent. So you can take on a hard, multi-year problem without watching over your shoulder for the next round or the next correction. And the team is small, around 65 engineers, so what you ship stays visible and your scope stays wide. How We Work Remote-first across the US and Canada, and we have been for years. That's not changing. We trust you to own your time and your outcomes, and we get everyone in a room together twice a year. Teams are small, pods of three to five engineers, so nothing you ship disappears into a committee. You'll work shoulder to shoulder with product, design and data, and with the partners across the business who rely on what you ship. You'll also stay close to customers yourself: sitting in on user interviews, watching session recordings, riding along with support. Here that's part of the engineering job, on a regular basis. How We Hire Three rounds, usually two to three weeks start to finish. Recruiter conversation (30 minutes). Trade context: what you want next, what we're building and straight answers on comp, team and remote. Technical screen (1 hour). One round, no AI, language-agnostic. Reach for Google when you normally would. We just want to see how you reason from first principles. Final round (4 hours). Four one-hour interviews: system design, AI-assisted coding with the tools you'd actually use here, product sense and culture and values. You'll hear back either way within 24 to 48 hours. If your timeline is tight, tell us and we'll move faster. Benefits Company-paid medical and fully covered dental and vision A 401(k) match Generous paid parental leave for birthing and non-birthing parents, plus a gradual return-to-work program Winter Wonder Week, a paid company-wide week off at the end of the year A remote-work stipend Mental-health and wellness support A Few Things To Know We record and transcribe interviews to evaluate candidates, in line with applicable privacy laws. We expect you to use AI in your work and we welcome it in the process, but what you submit and say should reflect your own thinking. If you have a family member or close relationship with a Babylist employee, let your recruiter know. Official outreach only ever comes from an @babylist.com address

Location: United States What The Role Is We're hiring a Staff MLE for the Discovery team to own recommendations and personalization across Babylist's consumer experience — the homepage feed, product recs, search, and the ML-powered systems that make registry building feel effortless. Babylist was built on editorial recommendations: products chosen by people with deep baby gear expertise. That editorial foundation is a big part of why millions of families trust us. We're now building ML-powered personalization on top of it, using one of the richest first-party datasets in parenting. We're early in this work, and we have a real mandate. A small Discovery team has initial retrieval and reranking models live on parts of the site and a steady cadence of A/B tests. We are looking for a Staff MLE who has seen personalization done well at scale and can set the technical direction for where we go next. Registry building is the heart of the Babylist product. Every parent builds a list of dozens of products, from swaddle to stroller, with real stakes (a friend or family member is going to buy these things, and a baby is going to use them). As a universal registry, this registry also typically spans many retailers. This makes registry building on Babylist one of the most interesting personalization problems in consumer e-commerce: latent intent, life-stage progression, multi-stakeholder gift dynamics, deep declarative signal in millions of completed registries, cross-retailer datapoints, and a user who genuinely wants help. If you want to join a mature ML org and tune models at the margins, this isn't the right role. If you've worked inside a strong recommendations team, learned what good looks like, and want to build from zero-to-one at a company earlier in the journey, read on. What You'll Own You'll be the technical lead for personalization on the Discovery team, working alongside a PM, Engineering Manager, Senior MLE and fullstack software engineers. You set where our models go over the next year or two, sequence the bets that get there, and stay deep in building. A few examples of the problems you’ll get to shape: Product recommendations across every surface. Product detail pages, add-next recommendations after someone adds to their registry, vibes/aesthetic representations of products and the shared models underneath them. Personalizing the Feed. Deciding which products, editorial content and social content each family sees, and in what order. Personalizing search. Bringing what we know about a family into search results, and helping decide how much search should share retrieval and ranking with recommendations. Making Addie (our AI registry assistant) better at recs. Working with the team behind Addie so its product suggestions draw on the same models and signals as the rest of the site. In practice, you will: Take a fuzzy business problem from first sketch to production model, and own whether it moved the metric. Make the modeling and architecture calls that span surfaces and are expensive to reverse. Design the evaluation, offline and online, that tells us a model is good before and after it ships. Know when a rule or a simple baseline is the right first step, and when it's time to pursue more advanced approaches. Partner with product, design and data to help shape what's worth building. Coach engineers on the team, and more broadly be an ML expert and resource to the wider engineering org. Who You Are You’ve shipped recommendation and/or personalization systems that reached real users at scale within a consumer product, and can point to the business impact of your work. You’ve done this within a team that did ML well — you know what good looks like, and are motivated to bring that to a company earlier in their ML journey. You bring: Expertise in recommender fundamentals: for example, candidate generation vs. ranking, offline vs. online evaluation, cold start solutions, and handling position and popularity bias. Fluency in the Python ML stack and comfort owning models through deployment and monitoring, including contributions to our application backend. An ability to take an ambiguous problem and start moving before anyone hands you the full picture. A builder's instinct for early-stage ML. You understand effort vs. effectiveness tradeoffs and can advocate for when a rule beats a model, or a model is “good enough” for early learnings. An outcome and user focused mindset. You measure yourself by business impact, and you can connect a model change to user experience & registry adds, conversion or revenue. AI-native daily practice. You're already using AI to move faster and improve your output, and you stay curious about what's coming next. Compensation We post real numbers. For a US-based Staff Engineer, the starting base salary range is $233,500 to $290,700, plus a target annual bonus of 20 percent of base. That's total target cash of roughly $280,200 to $348,840. On top of that you get meaningful equity and a 401(k) match. Where you start in that range depends on your experience, and your pay grows from there with performance and scope. How We Build AI is the default here. Engineers run agentic sessions for most of the work, and a lot of the interesting engineering now lives in the scaffolding that makes the agents good: the eval harnesses, the curated context, custom review skills and fast CI. Agents also triage incidents and handle a big share of support. A human always owns the outcome. The architecture is intentionally simple: one Rails monolith, MySQL and few moving parts. That's deliberate. Simple infrastructure lets us move fast and lets AI reason about the whole system, so the hardest problems are the ones in front of customers. The Stack Core app: Rails, Packwerk, React, TypeScript, Sidekiq Mobile: iOS (Swift), Android (Kotlin) Data, search & events: MySQL, Snowflake, dbt, Airflow, Weaviate, WarpStream, Hex, Sigma Machine learning: deep learning, matrix factorization, retrieval & ranking, AWS SageMaker, MLflow AI & dev tooling: Claude Code, Devin, Linear, CodeRabbit, Warden, LangChain, Bedrock AgentCore, Maxim Infra & ops: AWS, Kubernetes, Terraform, GitHub Actions, Datadog, incident.io, LaunchDarkly Key integrations: Shopify (payments), Iterable (CRM) Why Babylist An engineer, expecting her first baby, couldn't find the registry she wanted. So she built it. That's how Babylist started, and it's still how we work: engineers solving problems for families. Becoming a parent is one of the biggest moments in a person's life. Millions reach it for the first time every year, making thousands of decisions and figuring it out as they go. That's who we build for, and we're a long way from done. Ten million people give gifts through Babylist every year. We did more than $750M in revenue in 2025, up 45 percent over the year before, and we've been profitable for eight years while staying independent. So you can take on a hard, multi-year problem without watching over your shoulder for the next round or the next correction. And the team is small, around 65 engineers, so what you ship stays visible and your scope stays wide. How We Work Remote-first across the US and Canada, and we have been for years. That's not changing. We trust you to own your time and your outcomes, and we get everyone in a room together twice a year. Teams are small, pods of three to five engineers, so nothing you ship disappears into a committee. You'll work shoulder to shoulder with product, design and data, and with the partners across the business who rely on what you ship. You'll also stay close to customers yourself: sitting in on user interviews, watching session recordings, riding along with support. Here that's part of the engineering job, on a regular basis. How We Hire Three rounds, usually two to three weeks start to finish. Recruiter conversation (30 minutes). Trade context: what you want next, what we're building and straight answers on comp, team and remote. Technical screen (1 hour). One round, no AI, language-agnostic. Reach for Google when you normally would. We just want to see how you reason from first principles. Final round (4 hours). Four one-hour interviews: system design, AI-assisted coding with the tools you'd actually use here, product sense and culture and values. You'll hear back either way within 24 to 48 hours. If your timeline is tight, tell us and we'll move faster. Benefits Company-paid medical and fully covered dental and vision A 401(k) match Generous paid parental leave for birthing and non-birthing parents, plus a gradual return-to-work program Winter Wonder Week, a paid company-wide week off at the end of the year A remote-work stipend Mental-health and wellness support A Few Things To Know We record and transcribe interviews to evaluate candidates, in line with applicable privacy laws. We expect you to use AI in your work and we welcome it in the process, but what you submit and say should reflect your own thinking. If you have a family member or close relationship with a Babylist employee, let your recruiter know. Official outreach only ever comes from an @babylist.com address

Location: Remote A Software Developer (Market Setup) takes a trading venue from a signed agreement to the first live trade on our platform, then makes the next venue happen sooner. What You Own: one venue, stage by stage: Scoping. Read the venue's specifications; list what the platform has and what is missing; estimate the setup.Scoping. Connectivity. Design it with infra: colocation or proxy, lines, addresses, multicast groups, firewall rules, failover. Protocol adaptation. Adapt our market data and order entry connectors to the venue's binary protocol: SBE, FAST, ITCH and OUCH, Eurex ETI and EOBI, LSE native, or the custom binary protocol a venue publishes for itself; sessions, recovery, throttles, reject handling. Product and risk parameters feed. Pull the venue's raw product definitions and risk parameters, margin rates, price limits, lot and tick tables, position limits, in whatever form they are published, and deliver them complete to the team that normalizes them; you are their source on what the venue's fields mean. Historical data. Convert the venue's archives and captured feeds into our format at volume; validate against live captures. Latency tuning. Measure the path with hardware timestamps against the venue's figures and the strategies' budget; tune host placement, NIC and kernel bypass settings, pinning, the connector's hot path, gateway choice; record the baseline for ops. Certification. Pass the venue's conformance tests and ours: replay, soak, order-flow scenarios, failover. Operations preparation. Write the operating instructions for the desk and ops: sessions and timings, limits, start and stop, failure modes and what to do in each, who to call at the venue. Walk the desk through the first day before it happens. Go-live. First live trade, hypercare, handover. What This Role Is Not: Not the core engine: the engine developers in the same team own it; you adapt and assemble. Not configuration: a venue needs code, in C++ and Python; an LLM writes much of it, you specify, review and test all of it. Not normalization: another team maps the venue's products and risk parameters onto our model; you deliver the raw feed they map from. Not 24/7 operations: the desk and ops run the venue after handover; you write what they run. Not strategy work: researchers decide what and how to trade; you make the venue tradable. What We Look For In You: Solid understanding of data structures, algorithms, and object-oriented programming in C++ Understanding of Linux system internals Decent level of written and spoken English to work in an international environment Performs well in a remote/hybrid team with variable, project-based responsibilities Nice-to-have: In-depth knowledge and expertise with low latency/real-time development with sub-microsecond latency. LockFree containers and thinking pattern C programming knowledge (written drivers for Linux system, worked with the core etc.) Why Should You Join Our Team? Full focus on results with no bureaucracy Great challenges with fast feedback loops A welcoming group of highly qualified international professionals Cutting-edge hardware and technology Work remotely from anywhere in the world Access any of our global offices anytime Flexible schedule 40 paid days off Competitive salary