Senior Machine Learning Engineer

You’ll be the bridge between research and production.

Partnering closely with researchers, you’ll ensure experimental code is production ready, integrate models into our monorepo, build shared libraries and services, and create the tooling and processes that let multiple model variants ship safely and quickly.

Your work shortens the research-to-user loop, reduces duplication, and ensures our ML features are reliable, observable, and easy for other teams to adopt.

At the moment, this role is focused on:

• Research-to-Production Pipeline: Hardening experimental models (containerisation, tests, CI/CD), making them deployable for real users.
• Library development: Converting experiments into well-factored libraries with clear APIs, dependency hygiene, and versioning—so teams can import rather than copy-paste.
• Multi-Variant & Parallel Execution: Enabling multiple model variants to run in parallel (for canaries, A/Bs, and rollbacks) across our image-generation and related codebases.
• Developer Experience & Documentation: Creating templates, examples, and guidance; offering supportive, high-signal communication so others can adopt libraries confidently.
• Reliability, Observability & Cost: Instrumenting services with metrics/logging/tracing, setting SLIs/SLOs, and optimising inference performance and spend.

Primary Responsibilities:

• Productionise research models: refactor, test, containerise, and integrate them into the monorepo for scalable reuse.
• Build and maintain inference services, SDKs, and shared libraries that standardise pre/post-processing and interfaces across variants.
• Create multi-variant runners and rollout frameworks (feature flags, canaries, A/B testing, automated rollbacks).
• Establish CI/CD workflows, artifact management, and reproducible builds for ML services and model assets.
• Add robust observability (dashboards, alerts) and reliability practices (load tests, chaos/resiliency checks).
• Optimise inference (batching, caching, quantisation/compilation, hardware utilisation) to reduce latency and cost.
• Partner with researchers and product engineers via code reviews, pair sessions, and clear documentation to accelerate adoption.
• Drive good engineering hygiene in the research codebase: testing strategy, dependency management, and de-duplication across multiple model variants.