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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.
Reasons to use Rodeo
I’m in my final year doing Economics and I don’t know whether to apply for grad schemes now or do a masters first. What do you think?
Honest answer — it depends on where you want to end up. A lot of top grad schemes (Big 4, civil service, banking) don’t need a masters. Let’s look at the ones you’d be competitive for now, and we can decide if a masters actually adds anything.
Also worth knowing: most autumn 2026 applications are open now. Timing matters more than you think.
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Graduate Consultant — 2026 Scheme
Why you're a good match
StrongYour economics background and your summer at a regional bank line up with what PwC looks for on the consulting scheme. Applications close in four weeks.
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Every day your agent scans the market matching roles against what actually matters to you, not just keywords on a CV.
Why you're a good match
You’ve got the grades and the economics background, and your bank internship is exactly the experience this scheme looks for. Apply soon — deadlines close within the month.
Experience fit
Your summer at the bank plus your econometrics coursework map directly to the day-one responsibilities on this scheme — client modelling, market briefings, and deal support.
Only hits
No noise. No "maybe this fits." Just roles with a clear explanation of why they're right — and where to focus when applying.
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.


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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.
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