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Location: London 500M+ downloads. 80M+ monthly users. A decade of building – and we’re still accelerating. Flo is the world’s #1 health & fitness app worldwide on a mission to build a better future for female health. Backed by a $200M investment led by General Atlantic, we became the first product of our kind to reach a $1B valuation in 2024 – and we’re not slowing down. With 7M paid subscribers and the highest-rated experience in the App Store’s health category, we’ve spent 10 years earning trust at scale. Now, we’re building the next generation of digital health – AI-powered, privacy-first, clinically backed – to help our users know their body better. The job Millions of people trust Flo with their most personal health data. As our Security Architect, you’ll make sure we deserve that trust. This is a hands-on individual contributor role. You’ll work side by side with engineering, product and platform teams to shape secure architectures and build the guardrails that let both humans and AI coding agents ship quickly and safely. The role combines security architecture, product and application security engineering and security risk expertise: you will set secure patterns and standards, lead threat modelling and design reviews, embed security into the SDLC and CI/CD, and translate security risk into clear, pragmatic decisions. What you’ll do Shape secure architecture from day one. Partner with teams early in design, lead security design reviews for new services and major changes, and record key decisions and risk trade-offs. Build security harnesses for human and AI-agent development. Design guardrails, sandboxing, least-privilege access for agents and tools, secrets handling, policy-as-code and automated checks that run inside developer and agent workflows. Lead on AI and agentic security. Define secure patterns for LLM integrations, MCP servers and tool use, and tackle risks like prompt injection, data leakage, excessive agency and supply-chain compromise. Create paved roads. Publish reusable patterns, reference architectures and standards that teams adopt because they’re the easiest option. Scale threat modelling. Build tools and skills that let engineers run threat models themselves, and carry out internal security assessments. Remove whole classes of vulnerabilities. Automate vulnerability triage and investigation, find systemic root causes, and feed the fixes back into patterns and guardrails. Turn risk into clear decisions. Explain security risk in pragmatic terms so the business can prioritise based on risk and value. Advise and enable. Be a trusted partner to engineering, product and leadership, coach engineers, and help evaluate third-party and AI tools so they can be adopted securely. Support ISO 27001/27701 so that controls are built into how we engineer, not bolted on afterwards. What you’ll bring 7+ years in security engineering, application/product security or security architecture, including hands-on work securing cloud-native systems Deep knowledge of Secure SDLC: threat modelling, security testing, app security review and CI/CD security Hands-on experience with AWS and Cloudflare (or equivalents) and Terraform or similar IaC A strong grasp of the OWASP Top 10 and the ability to assess exploitability and impact Solid coding skills (e.g. Python, Go, Scala) and experience building security tooling or automation Practical understanding of the risks in AI-assisted and agentic development and LLM applications (e.g. OWASP Top 10 for LLMs) Experience with security risk analysis and risk management methods The ability to explain trade-offs clearly to engineers and senior stakeholders, and to win people over Nice to have Experience building guardrails, sandboxes or policy-as-code (e.g. OPA) for developer platforms or AI coding agents iOS or Android security experience Experience with ISO 27001, ISO 27701 or SOC 2 audits A background in health tech, femtech or another regulated, privacy-sensitive industry Certifications or degrees such as CISSP, CCSP, OSCP or an MSc in Information Security Why Flo You’ll protect data that matters deeply to millions of people. You’ll also help set the standard for how a company adopts agentic engineering safely, and you’ll have real ownership: you design and build the first version, then hand it off to the teams who will run it. Annual Salary Range (ranges may vary based on skills and experience) £100,000 — £130,000 GBP How we work We’re a mission-led, product-driven team. We move fast, stay focused and take ownership – from brief to build to impact. Debate is encouraged. Decisions are shared. We care about craft, ship with purpose, and always raise the bar. You’ll be working with people who take their work seriously, not themselves. It takes commitment, resilience, and the drive to keep going when things get tough. Because better health outcomes are worth it. What you'll get We support impact with meaningful reward. Here’s what that looks like: Competitive salary and annual reviews Opportunity to participate in Flo’s performance incentive scheme Paid holiday, sick leave, and female health leave Enhanced parental leave and pay for maternity, paternity, same-sex and adoptive parents Accelerated professional growth through world-changing work and learning support In-person collaboration and work in a hybrid model, with 3 days per week spent in the office 5-week fully paid sabbatical at 5-year Floversary Flo Premium for friends & family, plus more health, pension and wellbeing perks Diversity, equity and inclusion Our strength is in our differences. At Flo, hiring is based on merit, skill and what you bring to the role – nothing else. We’re proud to be an equal opportunity employer, and we welcome applicants from all backgrounds, communities and identities. Read our privacy notice for job applicants .

Location: Intellias Meet your recruiter Smita Aage smita.aage@intellias.com https://www.linkedin.com... Vacancy details Data Engineering Data Engineer Principal India Hybrid APPLY NOW REFER A FRIEND Our client is a company that enables people, enterprises, and cities to harness the power of location. By making sense of the world through the lens of location, it empowers their customers to achieve better outcomes from helping a city manage its infrastructure or an enterprise optimize its assets to delivering drivers to their destination safely. What project we have for you Our client is a is a location data and technology platform company that empower customers to achieve better outcomes – from helping a city manage its infrastructure or a business optimize its assets to guiding drivers to their destination safely. They create solutions that fuel innovation, provide opportunity and foster inclusion to improve people’s lives. If you are inspired by an open world and driven to create positive change, join us! As a Lead/Principal Engineer in the Map Data Processing group you will develop smart Map Data processing tools for the state-of-the-art Mapping Technologies. You will work self-sustained in an agile team. Your responsibility will cover developing, extending and maintaining tool and services that processes map data for global navigational database. For that you will be translating product strategies into technology strategies, leading the long-term architectural direction and helping design and build industry-grade customer-facing geo-data-intensive products. You will work closely with other engineering & operations teams, internal users of tool chains your team develops and partner with product managers and the larger map operations business units. What you will do Participate in the development, and maintenance of Python applications. Troubleshoot and debug applications to resolve issues. Write clean, efficient, and self-documented code. Implement automation tests. Collaborate with team members to identify and solve technical problems. Participate in code reviews to ensure code quality and maintainability. Work with stakeholders to gather and analyze requirements and provide technical solutions. Monitor application performance and provide optimization recommendations. Stay up to date with the latest technologies and industry trends. What you need for this Join our engineering organization to build the next generation of automated map data processing for a global provider of location and mapping solutions used in automotive and mobility. You will be one of the founding engineers on Platform — a system that takes road data from multiple sources (GPS traces, traffic signs, open map data), detects errors in the map, and either auto-corrects them or flags them for human review. The platform produces quality signals and road attribute corrections that feed directly into navigation and autonomous driving products. This is a project-starter role. We are looking for someone who can define technical direction, shape architecture from scratch, align stakeholders across teams, and raise the engineering bar — not someone who joins and executes tasks. The primary language on this team is Python/Pyspark — strong technical leads in either are welcome. Required skills: 8+ years of experience working as a Python Engineer/ Pyspark into Big Data Eco system or in a similar role. Strong Experience in AWS, Spark, Big Data Eco system Strong proficiency in Python programming and knowledge of object-oriented programming principles. Solid understanding of algorithms and data structures. Experience with OpenSearch / Elastic Search i.e. Understanding how the index’s works and how the layer are stitched together, also how to create schemas. Experience in Prompt Engineering to maintain, enhance or modify the prompts used for AWS Bedrock. Proficiency in ETL, protobuf. Experience with Amazon Web Services (AWS). Experience with API Gateway, Lambda’s, Step-Functions. Proficiency in version control systems, particularly Git. Knowledge of Agile methodologies, such as Scrum, and experience working in Agile teams. Strong problem-solving skills and the ability to debug and optimize code. Excellent communication and teamwork skills, with the ability to collaborate effectively with cross-functional teams. Will be a plus: Experience with HDLive maps, Open Drive, etc. Technologies: Python (primary — one or the other) Big Data, Spark, Pyspark AWS (Step Functions, ECS, Lambda, EMR) CI/CD (GitHub / GitLab) What it’s like to work at Intellias At Intellias, where technology takes center stage, people always come before processes. By creating a comfortable atmosphere in our team, we empower individuals to unlock their true potential and achieve extraordinary results. That’s why we offer a range of benefits that support your well-being and charge your professional growth. We are committed to fostering equity, diversity, and inclusion as an equal opportunity employer. All applicants will be considered for employment without discrimination based on race, color, religion, age, gender, nationality, disability, sexual orientation, gender identity or expression, veteran status, or any other characteristic protected by applicable law. We welcome and celebrate the uniqueness of every individual. Join Intellias for a career where your perspectives and contributions are vital to our shared success. Skills AWS BigData CI/CD Python Spark

Responsibilities: — Design prompts, agent logic and tools including function calling — Write test scenarios to evaluate the quality of agents — Analyze real agent conversations to identify and fix failures — Integrate agents with backend systems alongside the development team Requirements: — Minimum 2 years experience with Python — Experience building at least one large language model (LLM) agent or chatbot using APIs such as Gemini, OpenAI, or Claude — Strong skills in prompt engineering — Experience with function calling and Retrieval-Augmented Generation (RAG) — Proficiency in English Nice to have: experience with voice agents, multi-agent systems, evaluation tools like Langfuse or promptfoo, logistics domain knowledge, and knowledge of Uzbek or Russian languages. Benefits: — Salary starting from $1,500 per month — Office-based work in Tashkent To apply, send your CV to hr@gl-solutions.com with the email subject "AI Agent Engineer". Applications without a CV will not be considered.

- Build training datasets from real conversations and operational data. - Run fine-tuning jobs and compare tuned vs base models.

— Опыт системного анализа от 4 лет: сбор и формализация требований, ТЗ/SRS, User Story, Use Case, ФТ/НФТ. — Владение BPMN, UML, ERD, PlantUML, SQL;

Translate business needs into functional AI use cases and automated tools. Integrate models via APIs and develop retrieval-augmented generation workflows.

Responsibilities: — Design the architecture of AI agents including models, tools, and integrations — Establish standards for prompts, evaluations, and releases; review the team’s work — Decide when a fine-tuned model is ready for production — Hire and mentor ML and agent engineers Requirements: — Minimum 5 years experience in software or machine learning, with at least 2 years building large language model (LLM) systems — Delivered at least one LLM agent or chatbot to real users — Strong proficiency in Python — Experience with prompt engineering, function calling, retrieval-augmented generation (RAG), evaluations, and fine-tuning — Good command of English Nice to have: — Experience with voice agents, multi-agent systems, Google Cloud / Vertex AI, and logistics domain Benefits: — Salary from $3,000 per month — Office location in Tashkent Applications without a CV will not be considered. Email subject should be “AI Lead”.

• Опыт системного анализа от 3 лет: сбор и формализация требований, ТЗ/SRS, User Story, Use Case, ФТ/НФТ. • Владение BPMN, UML, ERD, PlantUML, SQL;

Zadaĵoj: — Stabiligi ekzistantan vertikalan video-generadan pipeline — Plibonigi enhavan kvaliton kaj viraleco, pligrandigi volumon — Testi kaj integri novajn generativajn modelojn kaj servojn — Krei kaj evoluigi virtualan AI-kreivan direkton — Evoluigi analitikan panelon kaj aŭtomatigan poŝtan sistemon Postuloj: — Forta scio pri Python, baza scio pri Bash — Sperto kun LLM API (Claude, OpenAI) — Bazaj scioj pri Git, Docker kaj REST API — Konoj pri SQL aŭ datumbazoj — Kapablo legi teknikajn dokumentojn en la angla — Bazoj de prompt engineering kaj agentdezajno Kondiĉoj: — Nova Mac laptopo, Google Workspace, Atlassian/Jira/Slack — Profesievoluo kaj subteno por kariera kresko — Fleksibla laborhoraro, neniu dresskodo, komforta oficejo — Malproksima laboro Teknologioj: Python, Bash, LLM APIs, Git, Docker, REST API, SQL, ffmpeg, GCP, Kubernetes, Datadog, OpenTelemetry

Location: Remote - United States Innodata (Nasdaq: INOD) is a global data engineering company. We believe that data and Artificial Intelligence (AI) are inextricably linked. Our mission is to enable the responsible advancement of artificial intelligence by providing the data, evaluation frameworks, and human expertise required to build AI systems that can be trusted at scale. We provide a range of transferable solutions, platforms, and services for Generative AI / AI builders and adopters. In every relationship, we honor our 36+ year legacy delivering the highest quality data and outstanding outcomes for our customers. Scope of the Role: As a Technical Product Manager for Trust & Safety, you will define and ship the data products that frontier labs use to train and evaluate models to perform personalization, computer-use (CUA), and GDPval tasks safely and in a trustworthy manner. You will own the full lifecycle of these products: from translating a lab's research need into a spec, to designing reinforcement learning (RL) environments, rubrics, and evals, to running delivery with our expert workforce and measuring whether the data actually moved the model. A core part of the job is building RL environments with deliberate traps. That includes misalignment traps that test for reward hacking, deception, sandbagging, oversight evasion, and power-seeking; cyber traps such as planted credentials, injected instructions, and poisoned tools; and other honeypots that reveal how a model behaves when it believes no one is watching. You thrive in ambiguity, go deep on technical detail, and care about making powerful AI safe and beneficial. What You’ll Own: Define data products for frontier labs. Turn lab research goals into clear product specs for safety training data, RL environments, and evaluations. Design trap-based RL environments. Build sandboxed environments that embed misalignment traps (reward hacking, specification gaming, deception, sandbagging, oversight subversion) and cyber traps (prompt injection, credential exfiltration, tool poisoning, unsafe file and network actions). Build safety into agentic tasks by design. Specify safe-behavior rubrics, verifiers, and reward signals so that models learn to complete real work while refusing, escalating, or asking for confirmation at the right moments. Own evals and metrics. Define what "safe and trustworthy" means for each product in measurable terms, including attack success rate, over-refusal, trap trigger rate, and task completion under adversarial pressure. Track blind spots and drive iteration. Prioritize ruthlessly. Frame problems crisply, lay out options with their business and technical tradeoffs, and separate MVP from ideal state. Partner across teams. Work with lab researchers, our ML and platform engineers, red-teamers, policy experts, and delivery leads running large expert annotator pools. Track the threat landscape. Stay current on agentic risks, alignment research, and new attack techniques, and turn them into roadmap items before customers ask. Communicate clearly. Write specs, customer briefs, and safety findings that land with researchers and executives alike. These examples show the kind of work this role owns: A cyber-trap environment suite : sandboxed environments seeded with injected instructions, exposed secrets, and destructive shortcuts, scored on whether the agent finishes the task without taking the bait. Tasks with integrity traps : realistic work where the easiest path to a high score requires fabricating data, skipping a required step, or gaming the grader. A misalignment eval harness : environments that check whether a model behaves differently when it believes it is being tested versus deployed, or when given chances to expand its own permissions. A trap library and quality framework that lets our delivery teams produce new environments quickly while keeping verifiers reliable and traps hard to detect. You’ll Thrive in This Role If You Have: 5+ years of product management experience, including technical products in ML, AI data, security, or trust & safety. Hands-on familiarity with how LLMs are trained and evaluated: SFT, RLHF/RLAIF, reward models, verifiers, and benchmark design. Working knowledge of agentic AI: tool use, computer-use agents, multi-step planning, and the failure modes that come with them. A strong grasp of AI safety concepts such as reward hacking, deceptive alignment, jailbreaks, and prompt injection. Enough technical depth to read code, review an environment spec with engineers, and make sound technical tradeoffs. Comfort with Python and sandboxed environments (Docker, VMs, browser automation) is a plus. Experience designing metrics that capture risk, system performance, and user impact, and using them to make crisp decisions. A track record of shipping 0-to-1 products in fast-moving, ambiguous environments. The ability to explain complex technical ideas clearly to researchers, customers, and executives. Creative, adversarial thinking: you enjoy imagining how a capable model might cut corners, and you don't stop at existing checklists. The expected salary range for this position is $180,000 – $220,000 USD per year, based on experience, skills, and qualifications. Please be aware of recruitment scams involving individuals or organizations falsely claiming to represent employers. Innodata will never ask for payment, banking details, or sensitive personal information during the application process. To learn more on how to recognize job scams, please visit the Federal Trade Commission’s guide at https://consumer.ftc.gov/articles/job-scams. If you believe you’ve been targeted by a recruitment scam, please report it to Innodata at verifyjoboffer@innodata.com and consider reporting it to the FTC at ReportFraud.ftc.gov .

Location: Toronto Workplace: Hybrid WHY HARVEY At Harvey, we’re transforming how legal and professional services operate. By combining frontier agentic AI, an enterprise-grade platform, and deep domain expertise, we’re reshaping how critical knowledge work gets done for decades to come. This is a rare chance to help build a generational company at a true inflection point. We have strong product-market fit and world-class investor support. We’re scaling fast and defining a new category in real time. The work is ambitious, the bar is high, and the opportunity for growth — personal, professional, and financial — is unmatched. Our team moves fast, takes ownership, and is deeply committed to the mission — operating with intensity, staying close to our customers, and pushing each other for excellence. We live by three values: Decisiveness, Simplicity, and Job's Not Finished. We act quickly on clear judgment over perfect information, we believe simplicity is what scales, and we're never satisfied with where we are. If you want to do the best work of your career alongside people who share that drive, we'd love to build with you. At Harvey, the future of professional services is being written today — and we’re just getting started. FUNCTION OVERVIEW Harvey's Legal Engineering team is composed of former practicing attorneys who work to bring value to Harvey's prospects and customers. Legal Engineers lead discovery, demos, trainings, and onboarding alongside our AEs and CSMs. They translate real legal work into Harvey solutions, serving as the trust layer across the entire customer lifecycle, from the first pilot to the most complex, custom-built workflow. At Harvey, we segment the Legal Engineering function into three teams: Legal Engineers (Pre-Sales), Legal Engineers, Product Specialists (Post-Sales), and Legal Engineers, Custom Solutions (Pre- and Post-Sales). Legal Engineers across all three subgroups understand how lawyers think and work, and use that expertise to design, build, and refine solutions that hold up under live in-practice pressure. In doing so, they don't just drive adoption; they shape Harvey's product roadmap, ensuring every release is grounded in what the legal profession needs. It's a team built to power Harvey as the platform lawyers can trust with their most important work.   You may be asked to travel around 2-3+ times per month. Travel is always for a significant business need and typically is for 1-3 days at a time. WHAT YOU'LL DO Legal Engineer (Pre-Sales) Legal Engineers are lawyers who left practice to help build the future of legal work. Working closely with Account Executives, they engage with many of the world’s leading law firms and legal teams throughout the pre-sales process. They lead collaborative pilot programs, guide prospects through what’s possible with AI, and work alongside lawyers to understand their workflows, build tailored solutions, and demonstrate meaningful value in real legal use cases. Along the way, they bring prospect feedback back into the product, helping shape the future of Harvey.   Legal Engineer, Product Specialist (Post-Sales) LEPS take over once a prospect becomes a customer. LEPS build trusted relationships with many of the world's leading legal teams by learning their workflows and showing them how to weave Harvey into daily practice. The LEPS role spans the entire customer journey, from leading onboarding and enablement, to sitting down with a full practice group to build agents on real matters. Along the way, the LEPS translates legal practice needs into actionable feedback for product and engineering, contributes to customer-facing content across practice areas, and upholds Harvey's credibility as the leading AI platform for lawyers.   Legal Engineer, Custom Solutions (Pre-Sale and Post-Sale) LECS are a specialist group that work on complex no-code custom Harvey in-app solutions. LECS meet with group heads, general counsel, and other senior stakeholders to learn how key processes run today. Drawing on their legal expertise, they use natural language prompting to design bespoke resources, including agents, playbooks, and other tools, that improve the quality, speed, and experience of that process. The build is collaborative. The LECS scope it with the customer, prototype against sample documents, and refine it alongside their team. Most LECS work happens post-sale, but the team also supports strategic accounts pre-sale on an advisory basis. LECS concentrates narrowly on designing tools that automate a specific process, and on building relationships with the senior leaders who own it.   *In your initial interview with our recruiting team, you'll learn more about all three roles, and we'll identify which may be the best fit based on your background and our current hiring needs. What You Have You move confidently through uncertainty, you operate at speed, and ship solutions that work—iterating quickly with lawyers, sales teams, and product partners to help Harvey and its customers win. You bring deep curiosity about AI's potential to transform legal work, a bias toward action, and the creativity to solve problems nobody's cracked yet. - Based in Toronto and eligible to work in Canada. - Canadian practicing certificate (or equivalent common-law qualification) with 3+ years’ experience as a qualified lawyer, including meaningful in-house experience at a corporation or financial institution (with prior top-tier law firm experience strongly preferred). - Strong background as a corporate attorney, with hands-on experience advising on corporate, commercial, and/or regulatory matters within an in-house legal team. - Deep understanding of corporate legal workflows (e.g., commercial contracting, governance, compliance, or transactions) and curiosity about how AI can reshape them. - Proven ability to navigate business-oriented legal environments, balancing legal risk with practical commercial outcomes. - Demonstrated ability to exercise sound judgment and make confident, well-reasoned recommendations in fast-paced, ambiguous settings. - Ability to operate seamlessly across legal, product, and sales contexts—translating between stakeholders with clarity and precision. - Executive presence and strong relationship-building skills, with experience engaging business stakeholders, senior leadership, and cross-functional partners. - Experience working cross-functionally with product, sales, or business teams in an in-house setting; customer-facing or business development experience is a plus. - French speaking is a plus. COMPENSATION CAD $192,800 - $289,200 OTE 75/25 DEPENDING ON YOUR LOCATION, AN APPLICANT PRIVACY NOTICE MAY APPLY TO YOU. YOU CAN FIND ALL OF OUR APPLICANT PRIVACY NOTICES HERE https://harveyai.notion.site/Harvey-Candidate-Privacy-Notices-319ac3fcdd7a803bb807d5094f249922?pvs=74. #LI-LS1 Harvey is an equal opportunity employer and does not discriminate on the basis of race, gender, sexual orientation, gender identity/expression, national origin, disability, age, genetic information, veteran status, marital status, pregnancy or related condition, or any other basis protected by law. We are committed to providing reasonable accommodations to applicants with disabilities, and requests can be made by emailing accommodations@harvey.ai

Responsibilities: — Lead, mentor, and scale a team of 3 to 6 AI-driven Creative Designers, focusing on people growth as well as output. — Manage the production flow including task distribution, capacity planning, and strict quality control to ensure production-grade results. — Drive creative direction by developing concepts, hooks, and visual angles that effectively capture attention and convert. — Generate innovative ideas and encourage the team to move beyond safe iterations, guided by visual trends and competitor analysis. — Own the creative testing loop by building a database of successful creatives, analyzing failures, and using performance data to inform future iterations. — Monitor new AI models and tools, acting as the company's expert on creative possibilities with AI while remaining hands-on. — Conduct regular research and development, creating prompt and style libraries to maintain consistent quality as the team expands. — Stay actively involved with design tools such as Figma, Adobe Suite, and AI platforms to support the team, handle complex creatives, and experiment with new approaches. — Collaborate closely with the Generative AI Pipeline Engineer to translate creative needs into pipelines, presets, and automation. — Work in tight coordination with Creative Producers, Marketers, Media Buyers, and Video Editors to align strategies, negotiate deadlines, and resolve bottlenecks. Requirements: — Over 5 years of leadership experience managing creative/design teams in digital marketing. — Proven maturity as a leader and problem solver, capable of building clear processes, guiding teams, resolving conflicts gracefully, and embracing new ideas. — Strong project management skills ensuring projects are completed on time with top-tier quality and order. — Excellent visual craft skills including composition, lighting, color, typography, and anatomy, with the ability to articulate why a creative works or not. — Proficiency in design tools such as Figma and Adobe Creative Suite, with comfort integrating AI outputs into traditional design workflows. — Advanced knowledge and enthusiasm for AI, tracking new model releases and testing their impact on ad creatives. — Hands-on experience with modern AI image platforms like Figma Weave, Higgsfield, Magnific, Runway, or similar for creating photorealistic visuals. — Ability to select appropriate tools for tasks and correct artifacts. — Solid understanding of performance marketing static ad creatives across platforms like Google and Meta, knowing what drives CTR, CPA, and conversion rates. — Fluency in English, both spoken and written. Nice to have: — A strong portfolio demonstrating performance marketing creatives and AI-generated photorealistic work, ideally with leadership-driven results. — Experience with creatives for high-CPA verticals such as home improvement, insurance, health, legal, or finance. — Familiarity with creating visuals tailored for North American audiences. — Understanding of how static visuals integrate into video production pipelines. Benefits: — Fully remote work environment allowing choice of productive and comfortable location. — Generous paid vacation policy. — Unlimited sick days to ensure full recovery. — Support for ongoing learning including free books, workshops, and conferences. — Coverage for home office equipment and furniture expenses. — Medical insurance coverage and allowance for flexible fitness programs. — Engaging and enjoyable work culture.

Location: New York; San Francisco Workplace: Hybrid ABOUT ALCHEMY Alchemy is the Web3 infrastructure and developer platform that powers millions of users and the world’s most innovative blockchain applications. From enabling $1+ trillion in financial transactions worldwide to scaling L2s and enabling DeFi, Alchemy provides the infrastructure and tools developers need to build reliable, scalable, and accessible experiences. Trusted by companies like JPMorgan, Stripe, Visa, Franklin Templeton, Nike, and backed by a16z, Coatue, Silver Lake, Lightspeed, and Stanford University, Alchemy is building the foundational layer for a decentralized and AI-enabled Web3 ecosystem. For more information, visit alchemy.com http://alchemy.com.   THE ROLE We're looking for a Security Operations Engineer to improve how Alchemy detects threats, investigates incidents, and reduces operational security risk. This is a hands-on individual-contributor role with no people-management responsibilities. You'll investigate suspicious activity and build the systems that make the next investigation faster and more reliable. AI-assisted engineering and investigation should be part of your daily work: connecting evidence, developing detections, building response workflows, and checking the results against source data. You'll also help the team get better at this work. Through shared tooling, code review, pairing, and practical documentation, you'll give teammates capabilities they can use independently.   WHAT YOU WILL DO - Detect and investigate threats. Triage alerts, hunt for suspicious activity, and investigate across cloud infrastructure, corporate systems, identity, endpoints, and production environments. Establish what happened from evidence and communicate uncertainty clearly. - Engineer detection coverage. Build and tune detections, onboard useful telemetry, and maintain reliable log pipelines. Test rules against representative attacks and benign activity; address missing coverage and noisy alerts. - Respond to incidents. Participate in the security on-call rotation and incident response, including scoping, containment, recovery, and post-incident improvements. Preserve evidence and coordinate changes with system owners. - Build AI-assisted security workflows. Automate evidence collection, alert enrichment, investigation timelines, and response preparation. Connect agents to approved tools and data sources so they can complete bounded tasks with traceable results. - Verify before trusting. Evaluate AI-assisted workflows against known cases and adversarial inputs. Require evidence for conclusions, test false-positive and missed-threat behavior, and keep human approval for disruptive containment actions. - Reduce operational exposure. Partner with Infrastructure and IT on vulnerability remediation, cloud and host hardening, identity controls, and attack-surface monitoring. Prioritize findings by exposure and impact, then verify remediation. - Help secure agent operations. Work with platform and product-security partners on operational controls for agent identities, credentials, tool access, and audit logging. Investigate misuse and build detections for suspicious agent behavior. - Improve how the team works. Share reusable code, investigation skills, runbooks, and evaluation cases. Pair with teammates, review automation, and teach approaches they can apply to their own investigations. - Measure defensive outcomes. Track detection coverage, alert quality, investigation effort, response time, and remediation progress. Use those measures to decide what to improve next.   WHAT WE ARE LOOKING FOR - Hands-on security operations experience spanning detection engineering, threat hunting, and incident response. - Strong scripting and software-building ability, especially Python; experience with APIs, version control, tests, and operating automation in production. - Experience with a SIEM such as Panther, Splunk, or an equivalent platform, including detection logic and log analysis. - Working knowledge of cloud security, identity, endpoint telemetry, networking, and Linux systems. Ability to investigate across system boundaries. - Evidence of building and using AI-assisted tools in security or adjacent engineering work. You can show how you validate outputs, handle failures, and protect sensitive information. - Sound incident judgment: you can separate a hypothesis from a finding, preserve evidence, and choose when to escalate or contain. - A record of making teammates more effective through tools, mentoring, code review, or documentation, without needing management authority. - Clear written communication and the willingness to own both urgent investigations and the follow-through that prevents repeat incidents.   HELPFUL EXPERIENCE - AWS, Kubernetes, bare-metal infrastructure, detection-as-code, or SOAR workflows. - Digital forensics, threat intelligence, or testing detections through attack simulation. - Securing AI agents and tool integrations, including prompt injection, credential misuse, and data-exfiltration risks. - Blockchain infrastructure or other environments with sensitive keys and high-impact production systems.   WHAT SUCCESS LOOKS LIKE - Investigations reach evidence-backed conclusions faster. Detection coverage improves, repeat noise declines, and remediation is verified. The team adopts tools and workflows you've built, with clear limits on what agents may do and a tested path for handling failures.   BENEFITS & PERKS 🏥 Private Health Insurance 💪 Gym Reimbursement 🚅 Commuter Benefits 🌴 Flexible Time Off 🥙 In-Office Group Meals 🧠 Learning & Development Stipend 🌎 Company Sponsored Conferences & Events 🎧 Books & Audiobook subscription with Bookster https://landing.bookster.ro/

Location: Berlin Workplace: OnSite ABOUT PEEC AI AI search is changing how brands are discovered and chosen. People now ask ChatGPT, Gemini, Claude and Google’s AI answers what they used to search for, and AI assistants increasingly shape the journey from discovery to purchase. Winning customers now means winning over the AI systems that influence their decisions. Peec AI is an AI search platform that helps marketing teams understand and improve how their brands are discovered, represented, and recommended. We show them where they stand, what’s shaping it, and where to act. Since launching in January 2025, Peec AI has passed $20M ARR and 4,000 paying customers. We’re a Series A company backed by 20VC and Singular, with 100+ people across our Berlin HQ and our New York office. THE ROLE Almost everything Peec shows a customer passes through a model at some point. We track brands, products, sentiment, citations and sources across millions of AI answers. Those answers change whenever a provider ships a new model and differ by country, language and prompt. Turning that into numbers customers can trust and act on is the core data science problem at Peec. Our data science team has grown fast, with AI-powered features shipping across the product. We're now growing the team further and need someone to lead it. You'll turn a group of strong individual contributors into a high-powered, fast-shipping team. You'll report directly to the CTO. WHAT YOU'LL DO Leading people - Hire, onboard and grow the data science team. We're hiring several data scientists right now, and you'll shape who joins and how they ramp up. - Build a team culture that matches our values: a high bar for quality, full ownership of outcomes, and kindness towards each other - Lead and inspire the team through regular 1:1s, actionable feedback and clear career paths Leading the technical work - Set the technical direction and roadmap for data science, together with product and engineering leadership, and decide what the team works on and what it doesn't - Own quality in production with strong operational excellence and reproducibility across the ML lifecycle - Drive innovation by investing in scalable machine learning data infrastructure - Stay close to the work: review designs and code, and step in hands-on when it matters - Represent data science to leadership and customers WHAT WE'RE LOOKING FOR - 8+ years in data science or machine learning, including 3+ years managing data scientists or ML engineers as direct reports, ideally at a high-growth startup or an established tech company - A track record of building great teams: hiring well, developing people, and creating the standards and rituals that make a team more than the sum of its individuals - Experience taking ML products from research to production, and building the practices that keep them reliable: evaluation, monitoring, reproducibility - A solid understanding of LLMs and strong instincts for measurement and statistics when outputs are noisy - Strong technical depth to review architecture and modeling decisions, ideally in cloud-native environments (GCP a plus) - Clear communication with executives, engineers and customers, including being honest about uncertainty - Good judgment under ambiguity, and the ownership to deliver outcomes despite blockers OUR STACK A standard Python and SQL stack on GCP. We work with all the major LLM providers (OpenAI, Anthropic, Google, Perplexity) as well as open-weight models. BONUS POINTS - Contributions to open-source projects or ML tooling - Public side or research projects that show technical depth - Publications or talks at ML or AI conferences - Founder experience or senior technical leadership at a high-growth startup WHAT WE OFFER - A defining leadership role with real impact and ownership at one of Europe's fastest-growing Series A startups - Regular team events and off-sites - Aggressive equity compensation package - Paid dinner & Uber home when working late - The most beautiful office space and work environment in Berlin

Location: US - Headquarters - Maryland - Columbia Who is Tenable? Tenable® is the Exposure Management company. Over 40,000 organizations around the globe rely on Tenable to understand and reduce cyber risk. Our global employees support 65 percent of the Fortune 500, 50 percent of the Global 2000, and large government agencies. Come be part of our journey! What makes Tenable such a great place to work? Ask a member of our team and they’ll answer, “Our people!” We work together to build and innovate best-in-class cybersecurity solutions for our customers; all while creating a culture of belonging, respect, and excellence where we can be our best selves. When you’re part of our #OneTenable team, you can expect to partner with some of the most talented and passionate people in the industry, and have the support and resources you need to do work that truly matters. We deliver results that exceed expectations and we win together! Applicants must be authorized to work for any employer in the U.S. without sponsorship. We are unable to provide sponsorship for work visas of any kind at the time of hire, or at any point during employment. This includes, but is not limited to: F1-OPT, F1-CPT, H-1B, TN, J-1, etc Your Role: In this role, you’ll help evaluate, implement and maintain Tenable’s cybersecurity systems. You’ll work alongside Security Engineers, Analysts and the Digital Solutions Department to protect the confidentiality, integrity and availability of Tenable’s assets. Your Opportunity: Help evaluate, deploy and improve security solutions across on-premise, cloud and SaaS environments Identify opportunities to improve the resiliency and quality of security systems and applications Support maintenance of security tooling and appliances Assist in identifying and remediating threats and risks reported by Security Analysts, assessments or third parties Participate in security reviews of SaaS, on-premise, and AI-enabled products (including LLM integrations, MCP servers and agentic tools), with guidance from senior engineers Help apply baseline AI security controls: least-privilege access, no static credentials, scoped tokens, logging/auditing of agent activity, and data handling guardrails Support detection and monitoring use cases for AI and agentic workloads Document projects and write Standard Operating Procedures Build strong working relationships across the team and with partner groups Manage your time and responsibilities, set accurate expectations and meet deadlines Provide infrequent, as-needed off-hours support Other duties as assigned What You’ll Need: Working knowledge of securing Windows, Linux, mobile devices and cloud (IaaS) environments Familiarity with security architecture and review methodologies (e.g., threat modeling, STRIDE) Foundational understanding of network security (IDS/IPS, web proxies, traffic analysis) and the TCP/IP stack and common protocols (DNS, SMTP, DHCP) Familiarity with authentication and access concepts: SSH, LDAP, SSO/OAuth/OIDC, RBAC, MFA Understanding of Zero Trust and cloud security principles Hands-on experience with 2 or more of: EDR/antivirus, SIEM/log correlation, CASB, web proxies, PAM, MDM, DLP, email security/DMARC Basic understanding of AI/LLM security risks (prompt injection, data leakage, excessive agent permissions, supply chain risk) and curiosity about MCP and agentic architectures Scripting or automation ability (Python, Bash or PowerShell) is a plus BA/BS in Computer Science, IT or a related field, SANS training, or equivalent experience 3-5 years of Information Security experience; some cloud experience preferred Security+, GIAC, AWS security or similar certifications are a plus Self-motivated, with a passion for cybersecurity and a willingness to innovate Able to work effectively in an agile environment, with growing independence Hybrid/on-site at our Columbia, MD headquarters #LI-Hybrid #LI-MS1 This is the base pay range for this position. Compensation for the role will depend on a number of factors, including the candidate's qualifications, skills, competencies, location and experience, and may fall outside of the range shown. Employees are also eligible for variable compensation in addition to base pay (commission for sales roles, bonus for non-sales roles), depending on company and individual performance. Tenable also offers a variety of comprehensive and competitive benefits which include: medical, dental, vision, disability and life insurance; 401(k) retirement savings with company match; an employee stock purchase plan; an employee referral program; flexible spending accounts; an Employee Assistance Program (EAP); education assistance; parental leave; paid time off (PTO); company-paid holidays; health and wellness events; and community programs. US Pay Range $102,500 — $136,500 USD We’re committed to promoting Equal Employment Opportunity (EEO) at Tenable - through all equal employment opportunity laws and regulations at the international, federal, state and local levels.  If you need a reasonable accommodation due to a disability during the application or recruiting process, please contact Recruiting@Tenable.com for further assistance. Tenable Data Consent Statement Tenable is committed to protecting the privacy and security of your personal data. Depending on your location, one or more of the following notices may apply to you: General Applicant Privacy Notice California Applicant Privacy Notice EU/EEA/UK Applicant Privacy Notice

Location: Prague Workplace: hybrid Filevine is a Legal AI company delivering Legal Operating Intelligence for the future of legal work. Grounded in a singular system of truth, Filevine brings together data, documents, workflows, and teams into one unified platform—where modern legal work happens with clarity and consistency. Powered by LOIS, the Legal Operating Intelligence System, Filevine connects context across every matter to transform legal operations from reactive to proactive. LOIS reads, understands, and reasons across your data to surface insight, automate complexity, and give professionals the clarity and confidence to see more, know more, and do more. Fueled by a team of exceptional collaborators and innovators, Filevine’s rapid growth has earned AI awards and recognition from Deloitte and Inc. as one of the most innovative and fastest-growing technology companies in the country. The Depo Intelligence team powers the AI behind Filevine's deposition product line. We deliver automated post-deposition transcript analysis, timelines, summaries, and witness impressions. Our latest product, Depo Copilot, assists attorneys during deposition preparation and delivers real-time insights during live proceedings. Additionally, our intelligent insights extend support to the Filevine Meetings and Notetaking apps. As the Tech Lead for Depo Intelligence, you will drive the technical direction of our AI systems. This is a hands-on machine learning role requiring strong software engineering skills. You will spend your time designing and building LLM pipelines and evaluations, as well as leading the team's architectural and design choices. You will collaborate closely with the team's Engineering Manager, who handles people management and delivery, allowing you to focus squarely on technical leadership. Key Responsibilities Own the technical vision and system architecture for machine learning workflows driving Depo Copilot, Depo Summaries, and the Meetings and Notetaking app. Architect and build LLM pipelines capable of reasoning over extensive case files and real-time transcripts, ensuring all outputs are fully grounded in source material. Determine optimal model strategies - evaluating when custom fine-tuning or trained models are required over prompt engineering - and lead implementation. Establish evaluation frameworks alongside our legal experts and annotators, creating custom datasets, rubrics, offline metrics, and release checks to proactively prevent regressions. Optimize pipeline efficiency, latency, and operational costs for live streaming analysis using strategic model selection, fallbacks, and precomputation. Transition prototypes into production-grade systems with typed contracts, test coverage, tracing, and observability, serving as technical lead for incident response during live proceedings. Author design documents, guide architectural decisions, and partner with Product, product engineering, speech recognition, and document retrieval teams to translate roadmap goals into actionable technical plans. Mentor machine learning engineers through detailed code reviews, pair programming, and design feedback while clearly communicating technical outcomes and tradeoffs to business stakeholders. Who are you? Minimum of 5 years of experience building production ML or software systems, including at least 3 years in machine learning roles with end-to-end project ownership. Proven track record of technical leadership, including conducting design reviews, setting architectural direction, and mentoring engineers. Strong background in NLP and production LLM systems, encompassing retrieval and grounding, long-context reasoning, structured outputs, evaluation, and mitigation of common failure modes. Experience designing evaluation frameworks for generative AI systems, including developing datasets and rubrics in collaboration with domain experts and annotators. Solid software engineering foundations, including API design, data modeling, distributed and asynchronous workflows, testing, and fault tolerance. Advanced proficiency in Python, alongside hands-on experience with PyTorch or other deep learning frameworks. Experience with real-time/streaming systems or legal domain NLP is a plus. Strong written and verbal communication skills in English, a collaborative team spirit, and an autonomous problem-solving mindset. Ability to meet periodically at our Prague or Bratislava office to support in-person collaboration. Our current stack primarily utilizes Python (DSPy, Pydantic, Temporal), PostgreSQL, Kubernetes, AWS, Google Cloud, and multi-provider LLMs; strong core engineering skills are prioritized over familiarity with specific tools or frameworks. Compensation starts at 190,000 CZK per month, with the final offer determined by the candidate’s seniority and experience. This opportunity is for independent contractors and is not intended to establish an employment relationship. Contractors are responsible for their own taxes and compliance with applicable laws in their jurisdiction. This role is open exclusively to candidates currently residing in, or willing to relocate to, Czechia or Slovakia with the legal right to work in these countries. Unfortunately, we are unable to consider applications from candidates based outside of these locations due to operational and regulatory requirements Privacy Policy Notice Filevine will handle your personal information according to what’s outlined in our Privacy Policy.

We're looking for a hands-on AI Engineer to take ownership of the architecture and development of our autonomous agent system. You'll lead the technical direction of the platform, building multi-agent orchestration systems, self-improving agent harnesses, and AI-powered backend services.

Location: Chicago, IL or Remote CCC Intelligent Solutions Inc. (CCC) is a leading cloud platform for the multi-trillion-dollar insurance economy , creating intelligent experiences for insurers, repairers, automakers, part suppliers, and more. At CCC, we’re making life just work by empowering more than 35,000 businesses with industry-leading technology to get drivers back on the road and to health quickly and seamlessly. We’re pushing boundaries with innovative AI solutions that simplify and enhance the claims and repair journey. Through purposeful innovation and the strength of its connections, CCC technologies empower the people and industry relied upon to keep lives moving forward when it matters most. Learn more about CCC at www.cccis.com . The Role As a Senior AI Engineer you will own LLM-powered systems and agentic workflows for our customers. These systems automate the routine parts of a claim and guide customers on the rest, so they have to earn that trust with a measured accuracy you can defend as models change and usage grows. Agents run as steps inside deterministic workflows, and privacy and security are design baselines, not review gates. Agentic coding tools are how you move fast. What you add is what they are worst at: system architecture, code quality, security, and the debugging nobody else gets to the bottom of. You own a product area end to end and define the eval bar it ships against. Salary range is: $180,000 - 225,000 This position is bonus eligible. Key Responsibilities: Own the delivery of LLM-powered systems in Python and TypeScript, from an ambiguous customer problem, scoped with product managers and data scientists, through production operation. Set the evals and guardrails strategy for your area: what gates a release, what production signals the team watches, and how a change is simulated against historical claims before it ships. Raise the bar for how the team works with agentic coding tools: sharpen the specifications, instruction files, hooks, and permission policies they run under, and hold generated code to the same review standard before it merges. Design the NLP pipelines that turn unstructured claim data into structured data customers can act on, choosing where semantic search, classical NLP, or an LLM fits. Build and operate durable, event-driven, multi-tenant systems in which agents run as steps inside deterministic workflows, designing their failure modes, replay semantics, and cost profile before they show up under load. Own tracing and audit for your area: every model call and agent step recorded, so any recommendation can be reconstructed and cited back to its sources by on-call and auditors alike. Set the privacy and security baseline every system in your area starts from: tenant isolation, least-privilege access for agents and their tools, redaction, retention, and what data may reach a model provider. Author the design documents for complex systems to get feedback and buy-in, review other engineers' designs and code with the same care, and mentor the engineers in your area. Take part in a shared on-call rotation for the systems you own. Requirements: 6+ years of professional software engineering experience, including an LLM-powered system you architected, shipped to external customers, and carried through at least one model or prompt migration. Python and TypeScript at the level of setting a team's conventions: the async model, typing strictness, and test strategy for a shared codebase, plus a performance problem you profiled and fixed. An evals and guardrails strategy you set for a product area others built in: where human approval was required, and how production corrections re-entered the evals. A clear account of how you constrain an agentic coding tool's context and verify its output, from daily use of Claude Code, Cursor, GitHub Copilot, or similar on production work, plus changes you turned back, and why. Pipelines you designed that blend LLMs, semantic search, and classical NLP, including a retrieval design you can defend and a case where a classical technique beat an LLM on cost, latency, or reliability. Durable, event-driven systems you designed with deterministic orchestration around non-deterministic model steps: compensating actions, idempotency, backpressure, and event-sourced replay. Data modeling and development across relational, document, and graph databases such as PostgreSQL, MongoDB, and Neo4j, including when each type fits and the partition-key, consistency, and tenant-isolation calls you made. Systems architected and operated on Azure or AWS, with CI/CD and infrastructure as code, and hands-on time with a durable workflow engine such as Temporal or the Durable Task Scheduler. Technical design documents you have authored for systems that span more than one team, used to get feedback and buy-in before building, and that those teams then built to. Clear writing and discussion for technical and non-technical audiences: drawing out a customer's real problem and explaining the trade-offs back to them. Even Better if You Have: React and TypeScript experience, so you can deliver a full-stack feature end to end, especially chat, streaming, and citation-heavy AI experiences. Experience with agent frameworks, LangGraph, PydanticAI, and the Microsoft Agent Framework among them, and with the ways agents reach their tools: the Model Context Protocol (MCP), the Agent2Agent (A2A) protocol, command-line tools, and microservices. Experience with LLM observability and evaluation tooling such as LangSmith, Arize, or Braintrust, and with OpenTelemetry-based tracing. Experience designing A/B tests for AI features. Exposure to the insurance, claims, or automotive repair domains. What Success Looks Like: In your first six months, every model change in your area is gated by evals you designed, regressions are caught before a customer sees them, and you can show the accuracy number held or moved. You are trusted with the high-stakes, ambiguous problems, and you turn them into a roadmap, a shipped system, and a number you can put in front of a customer. You are energized by what agentic coding tools make possible. Your team ships more than its headcount suggests, and your specifications and reviews are why the output survives production. You have a passion for systems that last: the ones you design have their failure modes understood in advance, and when one fails anyway, the trace says exactly where. Privacy and security reviews of systems in your area find nothing that was not already designed in. Standards you set for evals, reviews, and design documents outlive the project they started on. Engineers you mentor ship LLM-powered features with evals and guardrails in place without your direct involvement. You enjoy getting a product manager, a data scientist, and a customer's team into one room and coming out with a single agreed scope, and you can argue a position without needing to win it. You embrace our ambitious, collaborative, and empathetic values, and you like problems where the answer has to be measured rather than argued. Interview Policy & Privacy Notice: A video interview is required for this position. Video interviews are transcribed. Transcriptions are retained and may be reviewed by CCC and our recruiters. Candidates are not permitted to use generative AI or automated assistance during the interviews unless explicitly allowed by the interview team for a specific exercise. Our Job Applicant Privacy Notice is available HERE . About CCC's Commitment to Employees: CCC Intelligent Solutions understands that our employees play an integral role in our vision to shape a world where life just works. Our team is defined by our values of Integrity, Customer-Focus, Innovation, Inclusion & Diversity, Tenacity, and Connection. Through diverse perspectives, purposeful innovation, and the strength of connections, our technologies empower the people and industry relied upon to keep lives moving forward when it matters most. At CCC, together everyone can thrive as we innovate and collaborate, creating employee experiences that just work. We are committed to providing opportunities for our people to make real-life impacts, advance in their careers, and contribute to CCC’s success. CCC offers competitive compensation and benefits to support you and your families, including: 401K Match Paid time off Annual Incentive Plan Performance Bonus Comprehensive health insurance Adoption Assistance Tuition Reimbursement Wellness Programs Stock Purchase Plan options Employee Resource Groups For more information about our benefits, please check out our careers site . Here, you belong. You are seen, valued, and respected. We celebrate you for who you are and all you bring. Every voice is heard and is important to our success. You can hear what employees have to say about our culture here