Machine Learning Engineer, Agents

36 Labs is a seed-stage startup that started out of the UCL Machine Learning lab. We bootstrapped to 10 people, and now we're on the next leg of growth.

AI can generate almost any form of media, but it still can't judge what captures human attention. AI-generated content on social platforms is growing fast, every creative workflow will be agent-assisted or fully agentic. Our work is improving how models reason about creative decisions, learning from how audiences respond to content.

The role

You'll lead engineering on our creative harness: the agent system that plans, generates and refines content, and learns from how real audiences respond to it.

You'll have the autonomy to set how we build, measure and improve our agents, with scope to grow into technical leadership. You'll work closely with the CTO on direction, with backend engineers on the infrastructure the harness runs on, and with content engineers who use it every day.

What you'll do

• Design and build the creative harness end to end: planning, tool use, context, memory and multi-step execution across long-running generation work.
• Owning our evals: datasets, offline replays of real runs, scorers and model judges, and regression alerts that show when the harness gets better or worse.
• Run experiments and A/B tests on prompts, models, tools and agent design, and approach the results with statistical rigour.
• Close the loop between audience response and agent behaviour: turn performance data into the signals, features and training data the harness learns from.
• Choose and adapt models for each step: frontier APIs, open-weight models, and fine-tuning where it beats prompting on quality, cost or latency.
• Follow the research closely and bring new agent techniques and architectures into the harness to push what our models can do.
• Trace failure modes across the full agent loop, from planning to tool calls to final output.
• Set the standard for ML engineering: experiment design, reproducibility, documentation, and guidance for other engineers building the harness.

What we're looking for

• BSc, MSc or PhD in Computer Science, Mathematics, Statistics, Physics or a related field, or the equivalent in the form of experience. Strong Python.
• 4+ years in ML engineering or data science, including production systems you owned end to end.
• Built and shipped an agent system that real users or workloads depend on: models calling tools, planning across steps and acting on the results. A chat interface over retrieval is not enough on its own.
• Strong foundation in statistics, experimental design and research: the ability to differentiate improvements from noise.
• Built evaluation systems for ML or LLM behaviour, turning ambiguous quality questions into metrics and decisions.
• Strong understanding of modern LLM techniques: context engineering, structured outputs, tool use and fine-tuning.
• Follow academic research closely, and have explored novel techniques or architectures for agents that push the limits of what models can do.
• Comfortable across the stack, from model-level experiments to deployed services and data pipelines.
• Led technical work: set direction, reviewed designs, and have mentored more junior colleagues.
• Systems or projects you've built and can discuss in technical depth.

Nice to have

• Post-training experience: SFT, RLVR, RLHF, preference optimisation or RL for agent behaviour.
• Ranking, recommendation or causal inference work, especially predicting engagement.
• Multimodal models: image, video or audio generation and understanding.
• Founding or early engineer at a startup, or equivalent ownership inside a larger company.

Our stack

Python, TypeScript, Postgres, LLM and generative media APIs, open-weight models.

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