Lead ML Engineer — Creative Harness & Agent Systems

36 Labs; Greater London; Hybrid; GBP 90,000 - 160,000; Full time

Job summary

36 Labs is developing an autonomous agent harness that plans, generates, and refines content, learning from audience engagement data. You’ll own the production code and drive implementation from day one, collaborating with the CTO, backend engineers, and content engineers to shape the system.

You will lead design decisions, set technical direction, and grow into technical leadership as you scale the harness across experiments and real user workloads.

Qualifications

• 4+ years in ML engineering, with end-to-end ownership of systems.
• Strong Python and production-grade software engineering practices.
• Experience shipping an agent system with models calling tools.
• Ability to design evals with rubrics and model judges.
• Experience running benchmarks and A/B tests.
• Mentored junior engineers and led technical work.

Responsibilities

• Design and build the creative harness end to end.
• Create sandboxes and memory layers for agents.
• Own evals with datasets, rubrics, and human labels.
• Build benchmarks to measure changes across runs.
• Run experiments and A/B tests on prompts and tools.
• Turn performance data into signals for learning.
• Choose and adapt models per step; frontier or open-weight.
• Follow research and bring new techniques.
• Trace failure modes across full agent loop.
• Set ML engineering standards: reproducibility and docs.

Skills

Python

ML engineering

Production code

Code review

Team leadership

Education

BSc/MSc/PhD

CS/Math/Stats/Physics

Tools

PostgreSQL

TypeScript

LLM APIs

Open-weight models

Job description

36 Labs is developing an autonomous agent harness that plans, generates, and refines content, learning from audience engagement data. You’ll own the production code and drive implementation from day one, collaborating with the CTO, backend engineers, and content engineers to shape the system.

You will lead design decisions, set technical direction, and grow into technical leadership as you scale the harness across experiments and real user workloads.