Member of Technical Staff - Agent Engineer

H

About H: H exists to push the boundaries of superintelligence with agentic AI. By automating complex, multi-step tasks typically performed by humans, AI agents will help unlock full human potential. H is hiring the world’s best AI talent, seeking those who are dedicated as much to building safely and responsibly as to advancing disruptive agentic capabilities. We promote a mindset of openness, learning, and collaboration, where everyone has something to contribute. About the Team: The Agent team defines new learning algorithms and agent paradigms to push the frontiers of agentic systems. We build upon foundation models and reinforcement learning to develop new approaches to train artificial general agents and work closely with the LLM/VLM and Safety teams to explore new directions. This is a heavily engineering-focused role embedded within the research team. You will be responsible for defining the architecture and building the robust, scalable systems that underpin our research efforts. Your work will translate cutting-edge research concepts into high-performance, production-quality platforms, enabling the next generation of agentic AI. Key Responsibilities: * Research & Leadership: Design and develop new agents, proposing new research directions, e.g., combining state-of-the-art RL with foundation models (LLMs/VLMs). * Algorithm & Systems Design: Design, implement, and scale complex, high-performance systems for training large-scale agents. This includes both the foundational infrastructure and the novel algorithms, reward models, and sophisticated training environments. * Research-to-Production: Collaborate closely with researchers and engineers to implement, test, and productionize new agent logics, learning algorithms, and system architectures. * Evaluation & Reliability: Create, manage, and scale massive benchmarks and evaluation systems to rigorously track agent capabilities. You will own system reliability, scalability, and observability for our entire research infrastructure. * Mentorship & Standards: Mentor and guide other engineers and researchers on the team, fostering technical excellence. You will establish and enforce engineering standards, tooling, and best practices for both code and research design. * Innovation: Conduct thorough code and design reviews, champion technical innovation, and proactively address technical debt to accelerate the R&D lifecycle. Requirements: * Technical Skills: * Senior Experience: Previous demonstrable role(s) as a Staff, Principal, or Senior Engineer (or equivalent Research Scientist) in a Frontier AI Lab with a proven track record of leading complex, end-to-end AI/ML projects from conception to production. * Education / Publication: Preferably PhD (or equivalent research experience) in Machine Learning, Computer Science, or a related field, preferably with a strong publication record (e.g., NeurIPS, ICML, ICLR) in Computer Science. * Core Expertise: Deep theoretical and practical expertise in Agentic AI and proven experience building, scaling, and shipping solutions involving foundation models (LLMs/VLMs). * Soft Skills: * Collaborative: Enjoys collaboration and thrives in a teamwork-oriented, fast-paced research environment. * High-Impact Communicator: Possesses impactful communication skills, with the ability to bridge the gap between research and engineering and articulate complex ideas clearly. * Mission-Driven: Genuinely eager to explore and solve the new engineering and research challenges at the frontier of agentic AI. * Bonus Skills: * Practical experience applying Reinforcement Learning to systems built on Large Language Models (LLMs). * Experience with distributed systems or cloud computing, preferably in AWS. * Familiarity with building complex simulation environments for agent training. * Experience with LLM training or fine-tuning. * Experience developing large-scale evaluation and benchmarking systems for AI models. * Experience in an agentic framework (e.g., LangChain, AutoGen, CrewAI, OpenAI SDK). * Expertise in system architecture, instrumentation, observability, and monitoring for complex, high-performance systems. Location: * Paris or London. * This role is hybrid, and you are expected to be in the office 3 days a week on average. * Please expect some travel between offices on a reasonable cadence (e.g., every 4-6 weeks). What We Offer: * Join the exciting journey of shaping the future of AI, and be part of the early days of one of the hottest AI startups. * Collaborate with a fun, dynamic, and multicultural team, working alongside world-class AI talent in a highly collaborative environment. * Enjoy a competitive salary. * Unlock opportunities for professional growth, continuous learning, and career development. If you want to change the status quo in AI, join us.

H

About H: H exists to push the boundaries of superintelligence with agentic AI. By automating complex, multi-step tasks typically performed by humans, AI agents will help unlock full human potential. H is hiring the world’s best AI talent, seeking those who are dedicated as much to building safely and responsibly as to advancing disruptive agentic capabilities. We promote a mindset of openness, learning, and collaboration, where everyone has something to contribute. About the Team: The Agent team defines new learning algorithms and agent paradigms to push the frontiers of agentic systems. We build upon foundation models and reinforcement learning to develop new approaches to train artificial general agents and work closely with the LLM/VLM and Safety teams to explore new directions. This is a heavily engineering-focused role embedded within the research team. You will be responsible for defining the architecture and building the robust, scalable systems that underpin our research efforts. Your work will translate cutting-edge research concepts into high-performance, production-quality platforms, enabling the next generation of agentic AI. Key Responsibilities: * Research & Leadership: Design and develop new agents, proposing new research directions, e.g., combining state-of-the-art RL with foundation models (LLMs/VLMs). * Algorithm & Systems Design: Design, implement, and scale complex, high-performance systems for training large-scale agents. This includes both the foundational infrastructure and the novel algorithms, reward models, and sophisticated training environments. * Research-to-Production: Collaborate closely with researchers and engineers to implement, test, and productionize new agent logics, learning algorithms, and system architectures. * Evaluation & Reliability: Create, manage, and scale massive benchmarks and evaluation systems to rigorously track agent capabilities. You will own system reliability, scalability, and observability for our entire research infrastructure. * Mentorship & Standards: Mentor and guide other engineers and researchers on the team, fostering technical excellence. You will establish and enforce engineering standards, tooling, and best practices for both code and research design. * Innovation: Conduct thorough code and design reviews, champion technical innovation, and proactively address technical debt to accelerate the R&D lifecycle. Requirements: * Technical Skills: * Senior Experience: Previous demonstrable role(s) as a Staff, Principal, or Senior Engineer (or equivalent Research Scientist) in a Frontier AI Lab with a proven track record of leading complex, end-to-end AI/ML projects from conception to production. * Education / Publication: Preferably PhD (or equivalent research experience) in Machine Learning, Computer Science, or a related field, preferably with a strong publication record (e.g., NeurIPS, ICML, ICLR) in Computer Science. * Core Expertise: Deep theoretical and practical expertise in Agentic AI and proven experience building, scaling, and shipping solutions involving foundation models (LLMs/VLMs). * Soft Skills: * Collaborative: Enjoys collaboration and thrives in a teamwork-oriented, fast-paced research environment. * High-Impact Communicator: Possesses impactful communication skills, with the ability to bridge the gap between research and engineering and articulate complex ideas clearly. * Mission-Driven: Genuinely eager to explore and solve the new engineering and research challenges at the frontier of agentic AI. * Bonus Skills: * Practical experience applying Reinforcement Learning to systems built on Large Language Models (LLMs). * Experience with distributed systems or cloud computing, preferably in AWS. * Familiarity with building complex simulation environments for agent training. * Experience with LLM training or fine-tuning. * Experience developing large-scale evaluation and benchmarking systems for AI models. * Experience in an agentic framework (e.g., LangChain, AutoGen, CrewAI, OpenAI SDK). * Expertise in system architecture, instrumentation, observability, and monitoring for complex, high-performance systems. Location: * Paris or London. * This role is hybrid, and you are expected to be in the office 3 days a week on average. * Please expect some travel between offices on a reasonable cadence (e.g., every 4-6 weeks). What We Offer: * Join the exciting journey of shaping the future of AI, and be part of the early days of one of the hottest AI startups. * Collaborate with a fun, dynamic, and multicultural team, working alongside world-class AI talent in a highly collaborative environment. * Enjoy a competitive salary. * Unlock opportunities for professional growth, continuous learning, and career development. If you want to change the status quo in AI, join us.

Tessl

Tessl is a fast-growing Series A startup based in London, founded by Guy Podjarny. We’ve raised over $100M from world-class investors including Index Ventures, Accel, GV, and Boldstart, and in 2025 we were ranked #2 in Sifted EU’s B2B SaaS Rising 100 and #20 in Sifted's AI 100. At Tessl, we are building the context layer for AI coding agents, and a platform for AI-native software development. As an early member of the team, you’ll help shape how we build, scale and support a company operating at the edge of AI and software development. OVERVIEW OF THE ROLE We're hiring a Research Engineer to join our AI Research (AIR) team. You'll work on the components that make the outer loop real: how agent harnesses orchestrate model behaviour, how we evaluate what's actually working, how pipelines turn production traces into the next round of improvement, and how we diagnose the failure modes that matter to real users. These aren't four separate workstreams — they're parts of one system, and we want people who see them that way. We expect you to sit close to customers — joining calls, watching sessions, reading traces — and to let real workflows shape your research priorities. You'll have meaningful autonomy and the resources to run substantial experiments where the bar for success is shipped impact. You'll report to our AI Research Lead, and collaborate closely with engineering, product, and design. WHAT WE'RE LOOKING FOR We're explicitly building coverage across four skill areas. You don't need to be strong in all of them — but you should bring depth in at least one: * Agent harness and orchestration design — how tools, context, and control flow combine to make a useful agent. * Agentic eval methodology — task and repo-level evals, dataset curation, the craft of measuring what actually matters. * Outer-loop and pipeline thinking — feedback loops, training-data flywheels, bandit-style optimisation, anything that goes beyond a single agent session. * Failure-mode analysis — instrumenting agents, reading traces at volume, surfacing patterns engineering can act on. ESSENTIAL * 4+ years shipping AI/ML products in a startup or applied industry setting, with recent hands-on experience with LLMs and agentic systems. * Demonstrated depth in at least one of the four skill areas above. * Strong product and customer instincts: comfort joining customer calls, watching session recordings, and letting real workflows shape what you work on. * Sharp evaluation judgement: benchmarks where they exist, vibes and quick prototypes where they don't, and the taste to know which is appropriate. * Experience building datasets for evaluation or training, including the pipeline work that goes with it. * Deeply curious about agents and excited about reshaping how software is built. NICE TO HAVE * A Masters or PhD in a relevant computational field. * Direct experience with coding agents or code-generation systems. * Background in RL, bandits, or other outer-loop optimisation frameworks applied to LLMs. * Experience building synthetic data, dataset infrastructure, or internal tooling that other engineers actually used. * A project you can show us (GitHub links welcome) and a thoughtful answer to "Why Tessl?" WHAT YOU'LL DO No two weeks will look the same. A flavour: * Sit in on a customer session, understand how their agents are failing, design an eval that captures it, and drive a fix through to shipped improvement. * Close a piece of the outer loop end to end: production signal in, dataset out, eval scored, harness change shipped, metric moved. * Own a slice of our eval infrastructure: dataset curation, harness configuration, runner, analysis, and the comms back to engineering. * Prototype a new harness or context configuration and measure whether it actually moves the needle on real customer tasks. * Dig through pages of agent traces, build the tooling you need to make sense of them, and brief the team on what you found. * Partner with product and engineering on near-term shipping problems by bringing research rigour. * Pull a recent paper apart, work out what's actually transferable to our platform, and turn it into a concrete experiment. YOU’LL BE SUCCESSFUL IF… In your first 3 months, you might have shipped a new eval suite for a real customer workflow, improved an agent harness based on trace analysis, or built a pipeline that turns production failures into reusable test cases. SALARY AND BENEFITS Competitive salary commensurate with experience. Health insurance extending to partners and dependents, pension contributions, and the rest of what you'd expect. Our office is a couple of minutes from King's Cross — pet friendly, with regular team lunches, drinks, and socials. We're hybrid, with Monday, Tuesday, and Thursday as the primary in-office days. APPLICATION PROCESS * Intro call to understand "Why Tessl?" and to tell you a bit about us. * A call with our AI Research Lead to understand your ways of working and how you use agents. * A 4 hour technical take-home exercise extending our one-shot implementation. * A half-day on-site session including whiteboarding and hands-on activities. * Leadership chats with our Head of People, Head of Engineering and CEO. We care deeply about the warm, inclusive environment we’re building at Tessl and we value diversity – we welcome applications from those typically underrepresented in tech. If you like the sound of this role but are not totally sure whether you’re the right person, do apply anyway! LEARN HOW WE THINK AND WORK * On Tessl, The AI Native Development Startup * Announcing skills on Tessl: the package manager for agent skills * Podcast Episode: The End of Fragmented Agent Context, Guy Podjarny Tessl CEO