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Location: San Francisco; New York Workplace: Hybrid ABOUT BASETEN Baseten powers mission-critical inference for the world's most dynamic AI companies, like Cursor, Notion, OpenEvidence, Abridge, Clay, Gamma, and Writer. By uniting applied AI research, flexible infrastructure, and seamless developer tooling, we enable companies operating at the frontier of AI to bring cutting-edge models into production. We're growing quickly and recently raised our $1.5B Series F https://www.baseten.co/blog/announcing-our-series-f/, led by Altimeter Capital, Conviction Partners, and Spark Capital. Join us and help build the platform engineers turn to ship AI products. THE ROLE We're looking for an Engineering Manager to lead part of our Inference Performance team. This team makes the world's most demanding AI workloads run faster and more efficiently on GPUs. You'll manage and grow a team of inference performance engineers working across the inference engine and runtime: kernels, scheduling, batching, KV-cache management, speculative decoding and prefill/decode disaggregation. This is a hands-on technical leadership role. You'll set direction, unblock hard problems and earn the team's trust by going deep on GPU performance, while also hiring, developing and supporting the people doing the work. Your team's output directly affects how fast our customers' models run and how efficiently we serve them. The team is scaling quickly, so you'll help shape how it is structured as it grows. EXAMPLE INITIATIVES Your team will work on these types of projects as part of our Inference Runtime team: - Agentic inference optimization: 50-90% faster engines https://www.baseten.co/blog/agentic-inference-optimization-faster-than-sota/ - Agentic Kernels in Production https://www.baseten.co/blog/agentic-kernels-in-production/ - Live draft model training for speculative decoding https://www.baseten.co/blog/live-draft-model-training-for-speculative-decoding/ - The Baseten Inference Stack https://www.baseten.co/resources/guide/the-baseten-inference-stack/ RESPONSIBILITIES - Lead, mentor and grow a team of inference performance engineers through regular 1:1s, clear feedback, career development and performance reviews. - Hire top GPU and inference engineering talent, and build a strong, collaborative team culture as the runtime team scales. - Own the technical roadmap and execution for runtime performance work, balancing customer needs, new model launches and long-term platform investments. - Stay close to the technical work. Review designs, guide profiling and optimization efforts, and help the team reason from first principles about where time and memory go. - Drive the productionization of inference techniques such as quantization, speculative decoding, KV-cache reuse, chunked prefill and custom scheduling. - Turn performance wins into measurable outcomes: tokens per GPU-hour, utilization, latency and cost. - Help the team bring up and tune new model architectures on new hardware quickly, often in the same week they're released. - Partner with Infrastructure, Inference Platform, Kernels, Model APIs and customer-facing teams to set priorities, coordinate launches and ship wins. - Set high standards for engineering quality, benchmarking, operational excellence and incident response. REQUIREMENTS - Bachelor's, Master's, or Ph.D. degree in Computer Science, Engineering, Mathematics, or a related field. - Experience managing engineers, including hiring, mentoring, giving feedback and running performance reviews. - Experience leading or closely supporting GPU optimization teams in training, inference or recommendation systems. - Strong technical depth in GPU workloads, with a solid understanding of GPU architecture and performance tradeoffs. - Familiarity with ML libraries such as PyTorch, TensorRT or TensorRT-LLM. - A track record of driving roadmaps and shipping complex technical projects with a team. - Clear written and verbal communication, including the ability to align stakeholders across teams. NICE TO HAVE - Familiarity with inference engines such as vLLM, SGLang or TensorRT-LLM. - Experience with LLM optimization techniques (e.g., quantization, speculative decoding, continuous batching) in production. - Experience with GPU kernels (CUDA, Triton, CUTLASS, or similar). - Experience scaling a team through rapid growth at a startup. - A background as a hands-on performance or systems engineer before moving into management. BENEFITS - Competitive compensation, including meaningful equity - (U.S. only) 100% coverage of medical, dental, and vision insurance for employee and dependents - Flexible PTO policy including company wide Winter Break (our offices are closed from Christmas Eve to New Year's Day!) - Paid parental leave - Fertility and family-building stipend through Carrot - (U.S. only) Company-facilitated 401(k) - Exposure to a variety of ML startups, offering unparalleled learning and networking opportunities. Apply now to embark on a rewarding journey in shaping the future of AI! If you are a motivated individual with a passion for machine learning and a desire to be part of a collaborative and forward-thinking team, we would love to hear from you. At Baseten, we are committed to fostering a diverse and inclusive workplace. We provide equal employment opportunities to all employees and applicants without regard to race, color, religion, gender, sexual orientation, gender identity or expression, national origin, age, genetic information, disability, or veteran status. We are an Equal Opportunity Employer and will consider qualified applicants with criminal histories in a manner consistent with applicable law (by example, the requirements of the San Francisco Fair Chance Ordinance, where applicable).

Location: United States Who We Are MEMX is a technology-driven exchange operator founded by members to benefit all investors. We operate MEMX Equities, MEMX Options, and MX2 Options, and provide market technology to clients. Our mission is to create healthy competition and advocate for market structure reform that results in more efficient and sensible trading experiences for all investors. Description MEMX is seeking a Network Engineering Intern to learn how the networks behind our U.S. equities and options exchanges are built and supported. You’ll work alongside experienced engineers on hands-on projects and gain exposure to the reliable, low-latency infrastructure that supports trading. This internship position will be offered as hybrid with 1- day in person at our Data Center in North Bergen, NJ, and 2-days in person at our co-working office in Midtown -NYC . Work Schedule The intern will be expected to work the following schedule: Monday: 1:00pm – 10:00pm ET, in person at our North Bergen Data Center Tuesday: 10:00am – 6:00pm ET, remote Wednesday: 9:00am – 5:00pm ET, in person at our co-working office in Midtown-NYC Thursday: 9:00am – 5:00pm ET, in person at our co-working office in Midtown-NYC Friday: 9:00am – 5:00pm ET, remote Hours are subject to change based on operational needs. *MEMX has currently approved ONLY the following states for remote work: California, Colorado, Connecticut, Delaware, Florida, Georgia, Illinois, Kansas, Maine, Maryland, Massachusetts, Michigan, Nevada, New Jersey, New York, North Carolina, Pennsylvania, South Carolina, Texas, & Utah. What You’ll Be Doing Help the team monitor network performance and investigate alerts and connectivity issues Assist with network device configuration, testing, and documentation under the guidance of experienced engineers Assist with data center activities such as racking, network and hardware, running optical fibers, etc. Contribute to projects involving network tools, automation, and infrastructure improvements Collaborate with teams across MEMX to understand how network engineering supports the exchange Requirements Current undergraduate student entering their junior or senior year, pursuing Computer Science, Information Technology, Network Engineering, or a related field Foundational understanding of networking concepts such as IP addressing, routing, and switching through coursework, projects, or personal interests Interest in troubleshooting and learning how large-scale networks operate Strong attention to detail and willingness to ask questions and learn from the team Exposure to Python, Linux, or Wireshark is a plus, but not required Ability to work in person at our Midtown Manhattan office during the summer Permanent residence in one of MEMX’s approved states for remote work during the internship What You’ll Gain Hands-on experience with the network infrastructure that supports a securities exchange Exposure to various Options and Equities exchange platforms and how network connectivity supplements these platforms Exposure to network monitoring, troubleshooting, and day-to-day operations Learn how customer connections are set up and supported Mentorship and guidance from experienced network engineers Opportunities to collaborate with technology teams across MEMX and connect with fellow interns Why MEMX At MEMX, you’ll contribute to meaningful work alongside experienced professionals while developing your skills and learning how our exchanges operate. As part of our summer internship program, you’ll also have access to: Learning opportunities: Attend Lunch & Learn sessions, professional development workshops, and relevant courses Mentorship and support: Learn from a mentor and a designated buddy who can help guide you throughout the summer An intern cohort: Connect with interns working across other MEMX teams Social events: Get to know colleagues, with opportunities to connect with members of MEMX’s C-suite Regular feedback: Check in with your manager and HR to discuss your progress, ask questions, and get support Future opportunities: All interns are considered for potential internship extensions or a full-time opportunity after graduation, based on performance and business needs Pay Rate: $30/hourly for undergraduate students Equal Opportunity Statement MEMX is an equal opportunity employer. We are committed to creating a diverse and inclusive environment for all employees. All qualified applicants will receive consideration for employment without regard to race, color, religion, gender, gender identity or expression, sexual orientation, national origin, genetics, disability, age, or veteran status. Diversity Inclusion Statement At MEMX, we believe that diversity and inclusion are essential to driving innovation and success. We welcome and celebrate individuals from all backgrounds and perspectives, and we strive to create an inclusive culture where everyone can thrive.

Location: Remote Workplace: Remote ABOUT BASETEN Baseten powers mission-critical inference for the world's most dynamic AI companies, like Cursor, Notion, OpenEvidence, Abridge, Clay, Gamma, and Writer. By uniting applied AI research, flexible infrastructure, and seamless developer tooling, we enable companies operating at the frontier of AI to bring cutting-edge models into production. We're growing quickly and recently raised our $1.5B Series F https://www.baseten.co/blog/announcing-our-series-f/, led by Altimeter Capital, Conviction Partners, and Spark Capital. Join us and help build the platform engineers turn to ship AI products. THE ROLE As a Solutions Architect at Baseten you will partner closely with Sales and customers to translate business needs into technical solutions, run technical discovery, and guide repeatable deployments and proofs of value for customers. This role is a great fit for entrepreneurial, customer-facing technical professionals who want a front-row view into how modern companies adopt AI at scale, and who enjoy working across technical discovery, solution design, demos, deployment scoping, and hands-on customer implementations, in close partnership with Sales and Engineering. RESPONSIBILITIES - Partner with Sales on customer discovery calls (most often second calls, occasionally first calls for large accounts). - Lead demos and technical scoping to align on success criteria, architecture, and deployment approach. - Own benchmarking and repeatable deployments, including: - Handling standard deployment patterns and configurations across many modalities – LLMs, embeddings, image and video generation, Voice AI, etc. - Advising on tradeoffs like H100s vs B200s and latency-optimized vs throughput-optimized setups. - Driving consistent “playbook” style deployments for common models and use cases. - Become a power user of different runtimes such as vLLM, SGLang, and TRT-LLM and all the common configurations and tradeoffs between them - Drive POC and project execution, including: - Scoping POCs and keeping stakeholders aligned on timeline, deliverables, and next steps. - Acting as the “ringleader” or project manager for POCs. - Pulling in Forward Deployed Engineering (FDE) support when deeper or more complex technical work is needed. REQUIREMENTS - AI/ML background and the ability to credibly discuss AI/ML topics with technical stakeholders. - Strong customer-facing communication skills, including the ability to run structured discovery and clarify ambiguous requirements. - Technical depth to scope solutions, without needing to write production code. - Ability to script and prototype as needed, including comfort “vibe coding” to move quickly in technical workflows. NICE TO HAVE - Experience running or supporting benchmarks for ML inference deployments. - Familiarity with infrastructure tradeoffs relevant to inference performance and cost (for example GPU selection and latency versus throughput tuning). - Experience serving as a cross-functional technical lead for customer POCs, including coordination across Sales and Engineering. BENEFITS - Competitive compensation, including meaningful equity - (U.S. only) 100% coverage of medical, dental, and vision insurance for employee and dependents - Flexible PTO policy including company wide Winter Break (our offices are closed from Christmas Eve to New Year's Day!) - Paid parental leave - Fertility and family-building stipend through Carrot - (U.S. only) Company-facilitated 401(k) - Exposure to a variety of ML startups, offering unparalleled learning and networking opportunities. Apply now to embark on a rewarding journey in shaping the future of AI! If you are a motivated individual with a passion for machine learning and a desire to be part of a collaborative and forward-thinking team, we would love to hear from you. At Baseten, we are committed to fostering a diverse and inclusive workplace. We provide equal employment opportunities to all employees and applicants without regard to race, color, religion, gender, sexual orientation, gender identity or expression, national origin, age, genetic information, disability, or veteran status. We are an Equal Opportunity Employer and will consider qualified applicants with criminal histories in a manner consistent with applicable law (by example, the requirements of the San Francisco Fair Chance Ordinance, where applicable).

Location: Poland; Brazil; Peru; Sweden; Colombia; Argentina; Romania; Slovenia; Lithuania; South Africa; Czech Republic; Hungary; Mexico; Chile; Slovakia; Bulgaria Workplace: Remote BUILDING THE FUTURE OF OPEN FINANCE Payward - the parent company behind Kraken, NinjaTrader, Breakout, xStocks, Payward Services and CF Benchmarks - has spent the last 15 years building one of the most modern and globally accessible financial infrastructure platforms in the industry, built to advance an open, global financial system. THE TEAM   Payward Services is an enterprise infrastructure platform that enables companies to seamlessly integrate trading, payments, funding, and digital asset services through a single unified system. It provides coordinated liquidity, custody, compliance, risk management, and settlement capabilities, reducing operational complexity for fintechs, banks, brokerages, and payment providers. As a Software Engineer in Payward Services you'll build and scale the systems that make this platform work under the hood, from real-time coordination across services to the infrastructure enterprise partners depend on to move money and assets safely, at scale, every day. We’re looking for builders who thrive in ambiguity, move quickly with ownership, and care deeply about creating robust solutions.   THE OPPORTUNITY - Design and implement robust Rust services and libraries that power trading, payments, and crypto experiences with a focus on reliability, performance, and user impact. - Write reusable, testable, and highly efficient code that scales across teams and supports revenue-critical systems without compromising safety or security. - Collaborate on large-scale distributed systems architecture, translating evolving product requirements into resilient, low-latency solutions. - Work alongside hundreds of experienced Rust engineers, contributing to a high-performance, product-driven engineering culture.   WHAT YOU BRING - 3+ years of software engineering experience - Proficient in writing network services or asynchronous code in Rust - Have a security-first mindset during system design to ensure robustness - Demonstrate the ability to autonomously debug issues across the stack (OS, network, application) - Apply a solid understanding of distributed systems and technologies, including RPC protocols, Kafka, and Event-Driven Systems, to design scalable solutions - Collaborate effectively with product teams to gather requirements and constraints for designing appropriate systems - Take complete end-to-end ownership of systems and libraries   NICE TO HAVES - Experience building B2B focused products, ideally in the fintech or crypto space Unless a specific application deadline is stated in the job posting, applications are accepted on an ongoing basis. Please note, applicants are permitted to redact or remove information on their resume that identifies age, date of birth, or dates of attendance at or graduation from an educational institution. We consider qualified applicants with criminal histories for employment on our team, assessing candidates in a manner consistent with the requirements of the San Francisco Fair Chance Ordinance. OUR COMMITMENT Payward is powered by people from around the world and we celebrate the diverse talents, backgrounds, contributions, and unique perspectives that everyone brings to the table. We hire based on merit, seeking out people with the right abilities, knowledge, and skills for the job. We encourage you to apply for roles where you don't fully meet the listed requirements, especially if you're passionate or knowledgeable about crypto. We may ask candidates to complete job-related skills or work-style assessments as part of our hiring process. These assessments evaluate competencies relevant to the role and are applied consistently across candidates for similar positions. Results are considered alongside experience and interviews, and are not the sole basis for any employment decision. As an equal opportunity employer, we don't tolerate discrimination or harassment of any kind, whether based on race, ethnicity, age, gender identity, citizenship, religion, sexual orientation, disability, pregnancy, veteran status, or any other protected characteristic as outlined by federal, state, or local laws. Stay connected Follow us on Twitter https://twitter.com/krakenfx Learn on the Kraken Blog https://blog.kraken.com/#:~:text=Enter%20your%20email%20address Connect on LinkedIn https://www.linkedin.com/company/kraken-exchange/ Candidate Privacy Notice https://www.kraken.com/legal/candidate-privacy-notice

Задачи: — Повышение эффективности утилизации GPU: формирование гипотез, исследование способов повышения эффективности, участие в реализации и внедрении решений. — Формирование рекомендаций и лучших практик по повышению производительности GPU-инфраструктуры. — Оптимизация и профилирование: поиск узких мест в производительности и их устранение с помощью профилировщиков, оптимизация доступа к памяти, ядрам, времени ожидания и пропускной способности. — Развитие инструментов диагностики: создание и улучшение инструментов для выявления и устранения инфраструктурных проблем, влияющих на эффективность утилизации, стабильность и скорость GPU-вычислений для обучения и инференса. — Исследование и внедрение современных решений: изучение новейших подходов к организации инфраструктуры для обучения и инференса, оценка их эффективности и внедрение в проекты. — Анализ архитектуры, тестирование, интеграция: взаимодействие с разработчиками, ML-инженерами и системными архитекторами, участие в оценке аппаратных решений, предложение улучшений для будущих поколений GPU, разработка планов тестирования, формирование бенчмарков, анализ регрессий производительности. Требования: — Знание Python и опыт системного программирования, разработка библиотек или фреймворков. — Опыт работы с PyTorch и распределённым обучением через torch.distributed. — Применение подходов параллелизации (data parallelism, tensor parallelism, pipeline parallelism, expert parallelism) для распределённого инференса или обучения. — Интерес к LLM и MLOps, понимание задач и вызовов эксплуатации больших моделей в продакшене. — Опыт работы с GPU (NVIDIA) и CUDA: понимание архитектуры GPU, разработка или оптимизация алгоритмов под CUDA, использование Nsight, nvprof или аналогов. — Умение оптимизировать производительность GPU-приложений и повышать эффективность утилизации GPU. — Способность анализировать профили и метрики производительности. — Навыки чтения и оптимизации сложного кода. — Эффективная командная работа и готовность делиться знаниями. Будет плюсом: — Уверенное владение C/C++ или аналогичными низкоуровневыми языками. — Опыт работы с библиотеками RL-обучения для LLM: veRL, slime, NeMo-RL, SkyRL и другими. — Опыт работы с библиотеками инференса: vLLM, SGLang, TRTLLM. — Опыт оптимизации под реальные продакшен-нагрузки, работа с low-latency- или real-time-системами.

Специалист будет отвечать за проектирование и развитие торговой инфраструктуры: управление жизненным циклом ордеров, маршрутизацию, интеграции с биржами и брокерами, pre-trade контроль рисков. Роль предполагает техническое лидерство в направлении: принятие архитектурных решений, ревью кода и работу с командой разработки.

Location: Hong Kong, Hong Kong SAR; 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 We are looking for an experienced blockchain developer to join our team building the next generation of high-performance on-chain trading infrastructure. You will help design and implement a unified trading engine — supporting Perpetuals, Spot, and Prediction Markets under a single margin account — and contribute to ExchangeOS , an infrastructure layer that lets KYC'd institutions and Web3 entities stake to create and operate their own trading venues (DEXs) on top of our chain. What You’ll Be Doing Design and build a unified account and matching system that supports Perpetuals, Spot, and Prediction Markets — including on-chain order matching, cross-margin validation, settlement, and liquidation/risk engines (ADL, ADL-avoidance, ADL waterfall, ecosystem insurance fund mechanics). Build and optimize the core components of ExchangeOS : the staking/venue-creation framework that allows KYC-verified institutions and Web3 protocols to launch and operate their own dex on shared infrastructure. Drive performance engineering across the stack — parallel transaction execution, low-latency order pipelines, and throughput/latency optimization at the engine and chain level. Track cutting-edge blockchain and trading-system technology (e.g. Hyperliquid, dYdX, other high-performance perp DEX architectures) and apply best practices to our infrastructure. Partner with product and business teams to translate venue/exchange requirements into technical designs, and provide ongoing technical support. What We Look For In You Strong proficiency in Rust , with hands-on experience in high-performance, low-latency systems; experience with parallel/concurrent execution engines is a strong plus. Deep understanding of exchange/matching engine mechanics: order book design, margin systems, liquidation and ADL, settlement, and risk engines — familiarity with perpetual, spot, or prediction market products is a strong plus. Solid grounding in blockchain fundamentals (consensus, P2P networking, cryptography); experience reading core code of major L1/L2 systems (Ethereum, Optimism, Arbitrum, or high-performance L1s like Hyperliquid/Solana) is a plus but not the primary focus of this role. Strong systems-performance mindset: profiling, memory/allocator tuning, and designing for sub-millisecond latency at scale. Excellent teamwork, communication, and logical reasoning skills; genuine passion for trading systems and blockchain technology. 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! 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: San Francisco; Toronto; New York; Montreal; Seattle Workplace: Hybrid ABOUT BASETEN Baseten powers mission-critical inference for the world's most dynamic AI companies, like Cursor, Notion, OpenEvidence, Abridge, Clay, Gamma, and Writer. By uniting applied AI research, flexible infrastructure, and seamless developer tooling, we enable companies operating at the frontier of AI to bring cutting-edge models into production. We're growing quickly and recently raised our $1.5B Series F https://www.baseten.co/blog/announcing-our-series-f/, led by Altimeter Capital, Conviction Partners, and Spark Capital. Join us and help build the platform engineers turn to ship AI products. THE ROLE We're looking for distributed systems engineers and product-minded generalists to build the distributed runtime that powers large-scale LLM inference on Baseten. Our inference platform empowers customers to deploy and operate cutting-edge models with industry-leading performance, scalability, and reliability. It also powers Model APIs, our hosted endpoints for the latest open-source models. You'll work across the stack, from the developer experience customers use to deploy models, through the libraries behind features like tool calling and reasoning, down to the systems that orchestrate deployments on Kubernetes and route traffic efficiently. Your job is to make sure every model on our platform is fast, reliable, and cost-efficient. You'll join a small, high-impact team at the intersection of distributed systems, model performance, infrastructure, and product, helping define how developers use AI models at scale. This role is ideal for engineers who enjoy owning systems in production, solving hard integration problems, and making complex infrastructure simple and reliable for users.   EXAMPLE INITIATIVES You'll get to work on these types of projects on our Inference Platform: - The Baseten Inference Stack at NVIDIA Dynamo Day https://www.baseten.co/blog/nvidia-dynamo-day-baseten-inference-stack/ - 2x faster inference with KV cache-aware routing https://www.baseten.co/blog/how-baseten-achieved-2x-faster-inference-with-nvidia-dynamo/ - How Baseten multi-cloud capacity management (MCM) unifies deployments https://www.baseten.co/blog/how-baseten-multi-cloud-capacity-management-mcm-powers-cloud-self-hosted-and-hybr/#comparing-deployment-options-cloud-vs-self-hosted-vs-hybrid RESPONSIBILITIES - Build the infrastructure and orchestration systems that deploy and run large-scale distributed LLM inference, including routing, autoscaling, scheduling, and runtime management. - Design, build, and operate Model APIs, with a focus on advanced inference capabilities: structured outputs (JSON mode, grammar-constrained generation), tool/function calling, and multimodal serving. - Implement platform fundamentals such as API versioning, validation, usage metering, quotas, and authentication. - Instrument deep observability (metrics, traces, logs) and build repeatable benchmarks for speed, reliability, and quality. Help set best practices for testing, release automation, and operational excellence. - Debug and harden complex production systems spanning Kubernetes, distributed runtimes, networking, and GPU workloads to improve reliability and scalability. - Partner with Inference Performance engineers and other teams to make new optimizations broadly available to customers and easy to configure. - Own projects end to end, from architecture through deployment, monitoring, and iteration on customer feedback. Along the way, make thoughtful tradeoffs between performance, reliability, operational simplicity, and developer experience.   REQUIREMENTS - Bachelor's, Master's, or Ph.D. in Computer Science, Engineering, or a related field, or equivalent practical experience. - 3+ years building and operating distributed systems, backend infrastructure, or large-scale APIs where reliability, latency, and scale are first-class concerns. - A proven track record of owning low-latency, reliable backend services, including rate limiting, auth, quotas, metering, and migrations. - Infrastructure instincts with a feel for performance: profiling, tracing, capacity planning, and SLO management. - Comfort debugging performance and reliability issues across multiple layers of the stack, from application behavior down to runtime and infrastructure internals. - A strong sense of developer experience. You think about how systems are used, not just how they work. - Eagerness to learn new languages, frameworks, and systems, and a real interest in inference engineering. Prior ML or LLM experience isn't required, though experience with model serving or inference systems is a plus. - Excellent written communication and collaboration skills, including writing clear design docs and working across functions.   NICE TO HAVE - Experience with or contributions to LLM inference engines and frameworks such as vLLM, SGLang, TensorRT-LLM, TGI, or Dynamo. - Deep Kubernetes experience, including operators and custom resources, plus familiarity with service meshes or API gateways. - Experience with distributed scheduling, autoscaling, or service orchestration. - Experience operating GPU workloads in production. - A background in developer-facing infrastructure or APIs, or contributions to open-source infrastructure or ML systems. - Familiarity with observability tooling, CI/CD systems, or release automation. BENEFITS - Competitive compensation, including meaningful equity - (U.S. only) 100% coverage of medical, dental, and vision insurance for employee and dependents - Flexible PTO policy including company wide Winter Break (our offices are closed from Christmas Eve to New Year's Day!) - Paid parental leave - Fertility and family-building stipend through Carrot - (U.S. only) Company-facilitated 401(k) - Exposure to a variety of ML startups, offering unparalleled learning and networking opportunities. Apply now to embark on a rewarding journey in shaping the future of AI! If you are a motivated individual with a passion for machine learning and a desire to be part of a collaborative and forward-thinking team, we would love to hear from you. At Baseten, we are committed to fostering a diverse and inclusive workplace. We provide equal employment opportunities to all employees and applicants without regard to race, color, religion, gender, sexual orientation, gender identity or expression, national origin, age, genetic information, disability, or veteran status. We are an Equal Opportunity Employer and will consider qualified applicants with criminal histories in a manner consistent with applicable law (by example, the requirements of the San Francisco Fair Chance Ordinance, where applicable).

Location: San Francisco; Toronto; New York; Montreal; Seattle Workplace: Hybrid ABOUT BASETEN Baseten powers mission-critical inference for the world's most dynamic AI companies, like Cursor, Notion, OpenEvidence, Abridge, Clay, Gamma, and Writer. By uniting applied AI research, flexible infrastructure, and seamless developer tooling, we enable companies operating at the frontier of AI to bring cutting-edge models into production. We're growing quickly and recently raised our $1.5B Series F https://www.baseten.co/blog/announcing-our-series-f/, led by Altimeter Capital, Conviction Partners, and Spark Capital. Join us and help build the platform engineers turn to ship AI products. THE ROLE We're looking for inference performance engineers who want to make the world's most demanding AI workloads run faster and more efficiently. You'll work across the stack, from the inference engine and runtime through scheduling, serving, and routing. Along the way you'll apply techniques like prefill/decode disaggregation, speculative decoding, and KV-cache management. You'll reason from first principles about where time and memory go, find what's holding performance back, and close the gap. Your work directly impacts how fast our customers' models run and how efficiently we serve them. This role is ideal for someone who thrives in a fast-paced startup environment and is eager to make significant contributions to the exciting field of LLM inference.   EXAMPLE INITIATIVES You'll get to work on these types of projects as an Inference Performance engineer: - Agentic Kernels in Production https://www.baseten.co/blog/agentic-kernels-in-production/ - How we built the new fastest API for GLM-5.2 https://www.baseten.co/blog/how-we-built-the-new-fastest-api-for-glm-52/ - Live draft model training for speculative decoding https://www.baseten.co/blog/live-draft-model-training-for-speculative-decoding/#eliminating-traditional-bottlenecks - Making Kimi K3 Tokenization 18x faster https://www.baseten.co/blog/making-kimi-k3-tokenization-18x-faster-for-million-token-agentic-workloads/ - The Baseten Inference Stack https://www.baseten.co/resources/guide/the-baseten-inference-stack/ - Driving model performance optimization https://www.baseten.co/blog/driving-model-performance-optimization-2024-highlights/ RESPONSIBILITIES - Implement and productionize cutting-edge inference techniques, working deep in runtime internals. That includes quantization, speculative decoding, KV-cache reuse, chunked prefill, LoRA, guided generation for structured outputs, and custom scheduling and routing algorithms. - Profile and optimize inference end to end, from kernel launch overhead and memory layout up to request scheduling, prefill/decode disaggregation, and cache-aware routing. Run cross-layer investigations, such as tracing a tail-latency regression from request timing through routing and batching down to a kernel. - Turn performance into cost savings. Improve tokens per GPU-hour, raise utilization, and give customers and internal teams clear latency/throughput/cost tradeoffs. - Bring up and tune new model architectures on new hardware quickly, often in the same week they're released. - Build benchmarking frameworks that measure real-world performance across model architectures, batch sizes, sequence lengths, and hardware configurations. - Contribute upstream to open-source inference engines (vLLM, SGLang, TensorRT-LLM), and partner closely with model, infrastructure, and customer-facing teams to ship wins.   REQUIREMENTS - Bachelor's, Master's, or Ph.D. degree in Computer Science, Engineering, Mathematics, or related field. - Experience with one or more general-purpose programming languages, such as Python or C++. - Familiarity with LLM optimization techniques (e.g., quantization, speculative decoding, continuous batching). - Strong familiarity with ML libraries, especially PyTorch, TensorRT, or TensorRT-LLM. - Demonstrated interest and experience in LLMs. - Deep understanding of GPU architecture.   NICE TO HAVE - Proficiency in enhancing the performance of software systems, particularly in the context of large language models (LLMs) - Contributed to vLLM, SGLang, TensorRT-LLM, or another inference engine. - Worked on large-scale distributed serving: autoscaling, load balancing, multi-region or multi-cloud capacity. - Written or optimized GPU kernels (CUDA, Triton, CUTLASS, or similar) - Worked on quantization (FP8/FP4, AWQ, GPTQ) or speculative decoding in production. - Deep understanding of software engineering principles and a proven track record of developing and deploying AI/ML inference solutions. BENEFITS - Competitive compensation, including meaningful equity - (U.S. only) 100% coverage of medical, dental, and vision insurance for employee and dependents - Flexible PTO policy including company wide Winter Break (our offices are closed from Christmas Eve to New Year's Day!) - Paid parental leave - Fertility and family-building stipend through Carrot - (U.S. only) Company-facilitated 401(k) - Exposure to a variety of ML startups, offering unparalleled learning and networking opportunities. Apply now to embark on a rewarding journey in shaping the future of AI! If you are a motivated individual with a passion for machine learning and a desire to be part of a collaborative and forward-thinking team, we would love to hear from you. At Baseten, we are committed to fostering a diverse and inclusive workplace. We provide equal employment opportunities to all employees and applicants without regard to race, color, religion, gender, sexual orientation, gender identity or expression, national origin, age, genetic information, disability, or veteran status. We are an Equal Opportunity Employer and will consider qualified applicants with criminal histories in a manner consistent with applicable law (by example, the requirements of the San Francisco Fair Chance Ordinance, where applicable).

Location: San Francisco Workplace: OnSite ABOUT US: AI NEEDS A NEW INFRASTRUCTURE LAYER. WE'RE BUILDING IT AT MODAL. Every era of computing brought new workloads that previous infrastructure couldn't support: mainframes, databases, and the cloud. Each time, the company that rebuilt the layer underneath defined the decade. AI is no different, except it touches everything instead of one slice, and the window to build the layer underneath it is open right now. Our customers include category-defining companies like Ramp https://modal.com/blog/how-ramp-built-a-full-context-background-coding-agent-on-modal, Cognition, DoorDash, and Suno. They rely on Modal for instant GPU access, sub-second container starts, and native storage, so it's simple to serve low-latency inference, fine-tune models, and access production-ready sandboxes at scale. We've passed $1B in annualized run rate, up 15x from a year ago. In October 2026 we raised a $750M Series D led by Accel at a $15.75B valuation. Our team includes creators of popular open-source projects (e.g.,Seaborn https://github.com/mwaskom/seaborn,Luigi https://github.com/spotify/luigi), academic researchers, international olympiad medalists, and experienced engineering and product leaders with decades of experience. THE ROLE: Modal’s Inference Runtime team owns the container runtime stack used to run inference and training workloads across our fleet. We work at the boundary of Linux, containers, filesystems, storage, GPU drivers, and distributed systems. Our goal is to make demanding ML workloads start quickly, run efficiently, and remain securely isolated, whether they use a single GPU, hundreds of gigabytes of memory, or multiple GPUs connected with RDMA. We’re looking for a systems engineer who enjoys working deep in the stack. You’ll build production runtime infrastructure in Rust and Go, diagnose difficult Linux and performance problems, and help determine the architecture of Modal’s container platform. You’ll also work closely with maintainers of gVisor and contribute to the runtime itself when the right fix belongs upstream. WHAT YOU'LL WORK ON: - Make container startup, checkpoint, and restore dramatically faster for large inference and training workloads - Build multi-GPU and accelerator-aware snapshotting, including efficient handling of GPU memory and RDMA-enabled workloads - Design zero-copy and low-copy data paths between container memory, filesystems, storage, and Modal’s runtime - Optimize large snapshot pipelines using techniques such as parallel uploads, direct I/O, incremental snapshots, and more efficient memory handling - Improve container image and filesystem performance across EROFS, FUSE, page caches, overlay filesystems, and remote storage - Extend our sandboxed runtime to support new GPUs, drivers, profiling tools, and device capabilities across NVIDIA and AMD hardware - Debug complex failures involving system calls, virtual memory, process lifecycle, kernel behavior, GPU drivers, and container isolation - Safely roll out runtime and kernel changes across a heterogeneous fleet using compatibility controls, scheduling constraints, feature flags, and observability - Work across the runtime, scheduler, storage, and GPU infrastructure, and take ambiguous production problems from investigation through deployment YOU MAY BE A FIT IF: - You have strong Linux systems knowledge, particularly processes, virtual memory, filesystems, system calls, scheduling, namespaces, cgroups, and signals - You’ve built or debugged container runtimes, sandboxes, the Linux kernel, or similarly low-level infrastructure - You have strong programming ability in Rust, Go, or another systems language, and an interest in becoming productive in both Rust and Go - You’ve profiled and improved systems where memory movement, I/O, synchronization, or kernel interactions dominate performance - You’re comfortable debugging across abstraction boundaries, from application behavior down through runtimes, drivers, and the kernel - You can turn loosely defined production problems into well-designed, reliable systems - You want to own important infrastructure and work closely with both internal teams and customers NICE-TO-HAVE: Any of the following would be helpful, but none is required: - gVisor, runsc, runc, OCI runtimes, seccomp, or checkpoint/restore systems - Linux kernel development, virtualization, sandboxing, kernel modules, or device proxying - CUDA, ROCm, GPU drivers, accelerator virtualization, or GPU profiling - RDMA and high-performance networking - FUSE, EROFS, direct I/O, mmap, page-cache behavior, or storage engines - Large-memory or multi-GPU inference and training systems - Contributions to open-source systems software Prior gVisor or machine-learning experience is not required. We care more about strong systems fundamentals, curiosity, and the ability to learn unfamiliar parts of the stack. WHY THIS ROLE: The container runtime is directly on the critical path for inference performance. Improvements here can substantially reduce cold starts, increase token throughput, unlock new accelerator types, and make previously impractical workloads possible. This is an opportunity to work on unusually deep systems problems with immediate production impact. You’ll have room to shape the architecture, contribute to open-source runtime technology, and take ownership of foundational infrastructure used by every inference and training workload on Modal.

Location: New York Workplace: OnSite ABOUT US: AI NEEDS A NEW INFRASTRUCTURE LAYER. WE'RE BUILDING IT AT MODAL. Every era of computing brought new workloads that previous infrastructure couldn't support: mainframes, databases, and the cloud. Each time, the company that rebuilt the layer underneath defined the decade. AI is no different, except it touches everything instead of one slice, and the window to build the layer underneath it is open right now. Our customers include category-defining companies like Ramp https://modal.com/blog/how-ramp-built-a-full-context-background-coding-agent-on-modal, Cognition, DoorDash, and Suno. They rely on Modal for instant GPU access, sub-second container starts, and native storage, so it's simple to serve low-latency inference, fine-tune models, and access production-ready sandboxes at scale. We've passed $1B in annualized run rate, up 15x from a year ago. In October 2026 we raised a $750M Series D led by Accel at a $15.75B valuation. Our team includes creators of popular open-source projects (e.g.,Seaborn https://github.com/mwaskom/seaborn,Luigi https://github.com/spotify/luigi), academic researchers, international olympiad medalists, and experienced engineering and product leaders with decades of experience. THE ROLE: We’re looking for a Legal Operations Lead to join us as the first dedicated legal operations hire at Modal. You’ll own the systems, records, and workflows that let a small legal team keep pace with a high-growth tech company, and you’ll have an opportunity to build most of them from scratch. This is a great fit for a highly organized, systems-oriented operator who thrives in a fast-paced environment and wants to build operational foundations, not just maintain them. You’ll report to our General Counsel and collaborate closely with the Finance, BizOps, GTM, and People teams. WHAT YOU’LL WORK ON: - Build and maintain contract operations, including designing the contract lifecycle management workflows and approval logic, and continuing to iterate and improve those processes - Design a legal intake process to support and route legal requests from internal stakeholders - Make the legal function faster with AI, and build and configure tooling to make us more efficient - Manage corporate administration and record-keeping, including Board and stockholder matters, signature coordination, and building a knowledge management system and database - Be the point person for corporate compliance, including entity management, foreign and domestic qualifications and filings, and coordination with registered agents - Support stock administration and cap table hygiene - Manage outside counsel and legal spend YOU MAY BE A FIT IF: - You have 5+ years in legal operations, contract operations, or a corporate paralegal role, preferably at a high-growth technology company - You’ve built and documented processes from the ground up, not just executed existing ones - You have experience configuring workflows and templates within Ironclad or a similar CLM - You have experience with AI tools and good instincts about where they help and where they quietly introduce risk - You have sound judgment about what to handle and what to escalate NICE-TO-HAVE: - Exposure to corporate and equity administration - Familiarity with Carta or a similar equity administration platform

Location: New York Workplace: OnSite ABOUT US: AI NEEDS A NEW INFRASTRUCTURE LAYER. WE'RE BUILDING IT AT MODAL. Every era of computing brought new workloads that previous infrastructure couldn't support: mainframes, databases, and the cloud. Each time, the company that rebuilt the layer underneath defined the decade. AI is no different, except it touches everything instead of one slice, and the window to build the layer underneath it is open right now. Our customers include category-defining companies like Ramp https://modal.com/blog/how-ramp-built-a-full-context-background-coding-agent-on-modal, Cognition, DoorDash, and Suno. They rely on Modal for instant GPU access, sub-second container starts, and native storage, so it's simple to serve low-latency inference, fine-tune models, and access production-ready sandboxes at scale. We've passed $1B in annualized run rate, up 15x from a year ago. In October 2026 we raised a $750M Series D led by Accel at a $15.75B valuation. Our team includes creators of popular open-source projects (e.g.,Seaborn https://github.com/mwaskom/seaborn,Luigi https://github.com/spotify/luigi), academic researchers, international olympiad medalists, and experienced engineering and product leaders with decades of experience. THE ROLE: We’re looking for a Senior Commercial Counsel to join us as the first commercial lawyer at Modal. This is a high-impact role for someone who wants to draft and negotiate a wide variety of commercial agreements and build the templates, playbooks, and positions that guide our operations. You’ll report to our General Counsel and collaborate closely with the Sales, BizOps, Finance, and Engineering teams and help build the foundation for a small but growing legal team. WHAT YOU’LL WORK ON: - Own customer contracting, including MSAs, service orders, NDAs, DPAs, and BAAs, negotiating directly with customers and partnering with the Sales team throughout the full deal cycle - Lead vendor-side contracting, including capex procurement, SaaS tools, and consulting arrangements, among others - Build and maintain contract repositories, template libraries, and contract playbooks - Partner with Legal Operations on our CLM, including approval workflows and escalation logic - Review and revise operational policies (terms of service, privacy policy, acceptable use, and website terms) and customer-facing marketing materials YOU MAY BE A FIT IF: - You have approximately 5-10 years of combined law firm and in-house experience negotiating commercial technology agreements, with meaningful time supporting an enterprise sales motion - You have a JD and active bar membership in good standing in New York or California - You’re deeply familiar with SaaS contracting - You’ve worked extensively within Ironclad or a similar CLM - You can build templates and playbooks from scratch - You have working knowledge of privacy and regulatory data protection frameworks, such as GDPR and HIPAA - You have sound commercial judgment and know how to balance risk with the business needs of a high-growth tech company NICE-TO-HAVE: - Experience with infrastructure, compute, or datacenter supply agreements

Location: San Francisco Workplace: OnSite ABOUT US: AI NEEDS A NEW INFRASTRUCTURE LAYER. WE'RE BUILDING IT AT MODAL. Every era of computing brought new workloads that previous infrastructure couldn't support: mainframes, databases, and the cloud. Each time, the company that rebuilt the layer underneath defined the decade. AI is no different, except it touches everything instead of one slice, and the window to build the layer underneath it is open right now. Our customers include category-defining companies like Ramp https://modal.com/blog/how-ramp-built-a-full-context-background-coding-agent-on-modal, Cognition, DoorDash, and Suno. They rely on Modal for instant GPU access, sub-second container starts, and native storage, so it's simple to serve low-latency inference, fine-tune models, and access production-ready sandboxes at scale. We've passed $1B in annualized run rate, up 15x from a year ago. In October 2026 we raised a $750M Series D led by Accel at a $15.75B valuation. Our team includes creators of popular open-source projects (e.g.,Seaborn https://github.com/mwaskom/seaborn,Luigi https://github.com/spotify/luigi), academic researchers, international olympiad medalists, and experienced engineering and product leaders with decades of experience. THE ROLE: We’re hiring a Field Marketing Manager in San Francisco to build and run the programs that turn Modal’s target accounts into active customers. You’ll work closely with our Sales team to plan regional campaigns, run events for the engineers and technical leaders we sell to, and report on how those programs contribute to pipeline and revenue. This is a hands-on role for someone who has run account-based field programs at a technical company and wants to help shape the function as it grows. WHAT YOU’LL WORK ON: - Act as a strategic partner to Sales, aligning field programs with sales priorities and helping AEs create and accelerate pipeline in their territories - Develop and execute account-based programs for named accounts, working with Sales to identify key stakeholders and the right mix of touchpoints for each - Decide which industry conferences and sponsorships are worth investing in, and own what we bring, who we send, and whether we go back next year - Plan and host high-touch 1:1 and 1:few events for prospects and customers, such as executive dinners, roundtables, and technical workshops, in partnership with AEs - Own pre-event and post-event execution, including targeted invitations, meeting booking, and timely follow-up with Sales - Build co-marketing programs with VCs, accelerators, and ecosystem partners to reach their portfolio companies and customers - Track and report on program performance, including meetings booked, account engagement, and sourced and influenced pipeline, and use the results to plan future investment - Own the VIP experience at Modal’s flagship events, including our annual conference, Runtime - Build repeatable processes and playbooks as the team grows YOU MAY BE A FIT IF: - You have a track record of running field programs at a technical company and can point to specific ones and clear outcomes - You’ve worked directly with AEs and you’re happy sharing a number with them - You have good judgment about where to spend: a lot of sponsorships look better than they perform, and you can tell which is which - You’re organized and self-directed, and comfortable running many events, partners, and vendors at once - You’ve produced events across a range of formats, from small executive dinners to large conference sponsorships and brand events - You have strong attention to detail and a high bar for taste and the quality of every event and touchpoint - You’re comfortable with data and reporting, including CRM, attribution, and pipeline analysis - You’re familiar with ABM, intent data, and marketing automation tools - You’ve built AI into marketing workflows, such as account research, audience development, event operations, and reporting NICE-TO-HAVE: - Experience at an AI, developer tools, or cloud infrastructure company - Experience marketing to technical audiences such as engineers, ML teams, and technical executives - Experience bridging product-led and sales-assisted growth - Existing relationships with Bay Area VCs, accelerators, or AI communities

Location: New York, NY (HQ); San Francisco, CA Workplace: Hybrid ABOUT RAMP Ramp is building the smart infrastructure for finance teams, embedded in the transaction flow of every dollar a business spends. We automate how over $275B in annualized spend flows in and out of 85,000+ companies: authorizing payments, flagging risk, categorizing spend, and closing books. The problems are high-stakes, data-dense, and unforgiving. We hire people with high agency and high urgency. We look for slope over intercept. We care less about where you trained and more about what you’ve built. At Ramp, everyone is a builder who owns problems end to end and makes consequential decisions that shape the outcome. The median Ramp customer saves 7.5% and grows revenue 16% in their first year – far in excess of businesses operating without Ramp. We believe every ambitious company deserves the same. If you want to build systems that directly shape how companies move and manage billions, Ramp is the place to do it. ABOUT THE ROLE  Buying something for a business should not require finance teams to piece together requests, purchase orders, receipts, and invoices across disconnected systems. Ramp is building a procurement experience that helps employees get what they need, gives finance teams control over commitments, and helps businesses pay for what they actually received. As a Product Manager for Ramp’s Procurement offering, you will own the vision, strategy, and roadmap for Ramp’s core procurement capabilities, including sourcing, requisition, approval workflows, renewals, purchase orders, receiving, two- and three-way matching, and inventory management. You will work with Engineering, Design, and Data to turn complex purchasing and accounting requirements into products that are easy to start using and reliable as customers grow. AI creates an opportunity to rethink this work: build agentic buying experiences for employees, automate work for approvers and stakeholders, and businesses make better purchasing decisions. You will decide where automation can take work off a customer’s plate, where a direct workflow is faster, and where a person needs to review or approve an action. Success means customers get value quickly, adopt the product in their everyday work, and spend less time chasing information and correcting errors. This role requires working in person at our NYC or SF office at least two days per week. Please note that this role requires in-person presence at our NYC or SF office at least 2 days/week. WHAT YOU’LL DO - Own the strategy and roadmap for Ramp’s Procurement pod. Make clear choices about which customer problems to solve, how the capabilities fit together, and what to build next. - Build connected workflows across sourcing, requisition, approvals, renewals, purchase orders, receiving, invoice matching, and inventory management. Partner with Bill Pay, Accounting, and platform teams so purchasing records and decisions carry through to payment and customers’ ERP systems. - Spend time with finance teams, procurement leaders, purchasing operators, and employees. Turn observed friction into simpler experiences for first-time customers and powerful workflows for complex organizations. - Build AI-powered experiences that complete useful work, such as finding new vendors, managing a renewal, drafting a request from a contract, or helping resolve a receipt and invoice discrepancy. Prototype with customers and decide what should be automated, suggested, or handled through direct user controls. - Make automation dependable. Partner with Engineering and Data to define quality measures, test realistic customer scenarios, learn from corrections, and provide clear explanations, approval boundaries, audit trails, and recovery when something goes wrong. - Own outcomes beyond launch. Define and improve activation, repeat usage, time to complete purchasing work, and the accuracy of matching and exception resolution. Use data and customer research to choose the next iteration. - Lead execution with engineers and designers, make scope and sequencing tradeoffs, and align partner teams on dependencies. Work with Sales, Implementation, and Customer Experience to turn customer commitments into repeatable product capabilities and support successful adoption. WHAT YOU NEED - At least three years of product management or equivalent product-building experience, with evidence of owning a product area from customer discovery through launch and measurable adoption. - Strong product judgment and systems thinking. You can simplify complex workflows while preserving the controls and flexibility customers need. - Hands-on AI fluency, demonstrated through products, prototypes, or meaningful projects. You can reason about model quality, latency, cost, and user control, and choose where AI improves the experience. - Technical and analytical fluency, developed through a technical or analytical degree or equivalent practical experience. You can work with engineers on data models and integrations, use data to investigate problems, and build prototypes to test ideas. - Deep customer curiosity and strong design judgment. You can learn unfamiliar purchasing and finance workflows, distinguish recurring needs from one-off requests, and make the product easier to use. - A track record of leading cross-functional teams, setting priorities, making difficult tradeoffs, and delivering outcomes through others. - Clear written and verbal communication. You can explain a product direction, a complex workflow, and the reasoning behind a decision to technical and non-technical audiences. NICE-TO-HAVES - Experience with procurement, purchasing, inventory management, accounts payable, or accounting software, including purchase orders, receiving, matching, and exception handling. - Experience shipping AI-powered products used in production, including document understanding, agent workflows, and evaluation systems. - Experience with ERP integrations, multi-entity businesses, or enterprise approval and permission models. - Experience building products where accuracy and trust matter, with meaningful auditability, user controls, and recovery paths. - Experience at a high-growth startup, taking new products from initial concept to sustained use or scaling an existing product. BENEFITS AVAILABLE TO ALL FULL-TIME RAMP EMPLOYEES (GLOBAL) - Flexible PTO - Centralized home-office equipment ordering - Health and wellness stipend - Budget for intra-office travel - Weekly coffee stipend UNITED STATES - 100% medical, dental & vision insurance coverage for you, with partial coverage for dependents - One Medical annual membership - 401(k), including employer match on contributions made while employed by Ramp - Fertility HRA (up to $10,000 per year) - Parental leave: up to 16 weeks (birthing + bonding) or 8 weeks (bonding only) at 100% pay - Pet insurance - In-office perks: lunch, snacks, drinks, and more - Relocation expense coverage to NYC or SF (if needed) CANADA - Group medical, dental, and vision coverage through Sun Life - Life, AD&D, and disability coverage - Fertility drug coverage (up to $4,000 lifetime) - Group Retirement Plan with employer match (RRSP + DPSP) - Parental leave: up to 16 weeks (birthing + bonding) or 8 weeks (bonding only) at 100% pay, with additional time available at reduced pay - Employee Assistance Program and virtual care through Lumino Health UNITED KINGDOM - Private medical insurance through Freedom Elite - Virtual GP and at-home care via eMed x Livi - Workplace pension through Penfold, with salary sacrifice option - Parental leave: up to 16 weeks (birthing + bonding) or 8 weeks (bonding only) at 100% pay with additional time available at reduced pay REFERRAL INSTRUCTIONS If you are being referred for the role, please contact that person to apply on your behalf.   OTHER NOTICES Pursuant to the San Francisco Fair Chance Ordinance, we will consider for employment qualified applicants with arrest and conviction records.   Beware of recruiting scams: Ramp will only contact you through official @Ramp.com http://Ramp.com email addresses and will never ask for payment or sensitive personal information during the hiring process.   Ramp Applicant Privacy Notice https://ramp.com/legal/applicant-privacy-notice

Location: San Francisco, CA Workplace: OnSite ABOUT US At LangChain, our mission is to make intelligent agents ubiquitous. We build the foundation for agent engineering in the real world, helping developers move from prototypes to production-ready AI agents that teams can rely on. We began as widely adopted open-source tools and have grown to also offer a platform for building, evaluating, deploying, and operating agents at scale. With $125M raised at Series B from IVP, Sequoia, Benchmark, CapitalG, and Sapphire Ventures, we’re at a stage where we’re continuing to develop new products, growth is accelerating, and all team members have meaningful impact on what we build and how we work together. LangChain is a place where your contributions can shape how this technology shows up in the real world. Today, our platform includes LangSmith (Observability, Evaluation, Deployment, Fleet, and Sandboxes), our open source frameworks (LangChain, LangGraph, and Deep Agents), and the newly launched LangSmith Engine for autonomous agent improvement. We have 100M+ monthly open source downloads, 6,000+ active LangSmith customers, and 5 of the Fortune 10 use LangSmith in production (+ 35% of the Fortune 500 overall), including teams at Klarna, Clay, Coinbase, Workday, Lyft, Cloudflare, Harvey, Rippling, Vanta, LinkedIn, Monday.com, Nvidia, and Bridgewater. About the team SmithDB is LangChain's internal database team. We're building a storage and query layer purpose-built for AI observability and evaluation. Within six months we went from idea to a production system that offers industry leading performance and scalability for agent observability data. We're a small, fast team of systems engineers tackling genuinely hard problems: storage layout, query execution, compaction, and scaling toward trillions of agent traces. We develop in Rust, run on Kubernetes, and integrate tightly with S3/GCS/Azure Blob. There are no legacy constraints; this is a greenfield system with real production load and ambitious engineering goals.   About the role We're looking for a hands-on Engineering Manager to lead the SmithDB team. This is not a pure people-management role. You'll write production code, review PRs at the systems level, make architectural calls, and be in the weeds alongside your engineers. At the same time, you'll own team health, hiring, technical roadmap, and coordination with the broader LangSmith platform. The ideal candidate has deep systems or database engineering experience and genuinely prefers to stay technical while growing a team.   What you'll do - Write and review production Rust code across ingestion, query execution, and storage layers - Lead architectural decisions on storage format, compaction, indexing, and query planning - Drive performance investigations using memory and CPU profiling tools; own the path from profiling to shipped fix - Design and harden the distributed deployment of SmithDB services on Kubernetes (multi-tenant, high-throughput, low-latency) - Contribute to cloud object store integrations (S3, GCS, Azure Blob) and set the standard for how SmithDB manages data at rest and in flight - Build and maintain observability for the engine itself: metrics, tracing, debug tooling - Manage a small but growing team of systems engineers: set goals, run 1:1s, provide technical mentorship, and grow careers - Own the SmithDB technical roadmap in partnership with LangSmith product and engineering leadership - Communicate progress, risks, and tradeoffs clearly to the broader organization: you write concise, decision-ready updates   What you'll bring - 7+ years in systems or database engineering, with at least 2 years in a technical lead or engineering management role - Production Rust experience — you can write it, review it, and have opinions on how to structure it at scale - Deep understanding of database or storage engine internals: query execution, storage layouts, indexing, compaction - Proficiency in systems performance analysis: memory allocators, CPU hotspots, lock contention, async runtimes (Tokio) - Experience deploying and operating distributed services on Kubernetes in a production, multi-tenant environment - Familiarity with cloud object storage (S3-compatible APIs, consistency models, cost/performance tradeoffs) - Proven track record of managing engineers — you can recruit, retain, and level-up a team without losing your technical edge Nice-to-have: - Experience building or contributing to columnar storage formats (Parquet, Arrow), OLAP query engines, or time-series stores - Background in observability infrastructure or tracing pipelines (OpenTelemetry, ClickHouse, Prometheus) - Experience scaling a system from early-stage to hundreds of billions / trillions of records   Compensation Salary Range: $240,000-$300,000 USD 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.

• Вести крупные изменения в поиске end-to-end. Вы будете отвечать за бэкенд крупных инициатив — от идеи и RFC до выката в продакшен и анализа результата.

The role You will build and lead our Forward Deployed Engineering team: the engineers who connect estaie to the hospitality ecosystem and make those integrations work in production. You own how the team works, what it ships, and the quality bar it holds.

Опыт проектирования высоконагруженных распределённых систем: горизонтальное масштабирование, отказоустойчивость, балансировка нагрузки, кэширование, очереди, rate limiting, graceful degradation. Опыт встраивания ML-сервисов в корпоративную архитектуру: интеграция с API Gateway, IAM/SSO, корпоративными шинами, системами мониторинга и журналирования.