AI role · Open offer

ML Engineer — fine-tuning & adaptation

Cohere

Full-time
Senior
Model adaptation
Hybrid
London, England, United Kingdom · Hybrid
London, United Kingdom
£110k–£150k + equity

This listing is optimized for AI-native talent: clear skills and tools so candidates know if they fit before they apply—and so you spend less time screening mismatches.

4 skills3 tools

At a glance

How you'll work, where, and key listing dates.

Employment: Full-time
Workplace: Hybrid
Location: London, England, United Kingdom · Hybrid
Compensation: £110k–£150k + equity
Experience level: Senior
Team: Model adaptation
Benefits & perks

About this role

What the team is looking for and how this position fits.

Cohere is hiring a Senior ML Engineer — fine-tuning & adaptation on the Model adaptation team (Hybrid · London, United Kingdom). This is a hands-on role in LLM & generative AI—not a generic “AI enthusiast” post.

You will own LLM features, retrieval, agents, and evaluation end to end: problem framing, offline evaluation, safe rollout, and iteration from production signals. Success is measured with clear metrics (quality, latency, cost, or business KPIs)—not slide decks alone.

Cohere builds enterprise LLMs and embedding models with a strong London presence.

ML Engineer — fine-tuning: LoRA/SFT recipes, data curation, and adaptation for customer domains.

Missions

Run fine-tunes with clear eval lifts vs base models.
Improve data pipelines for instruction and preference datasets.

Stack & environment

Python, PyTorch, CUDA, Docker, Kubernetes.

What we look for

Fine-tuning or LLM training experience; London hybrid OK.

Skills we're hiring for

What each capability means for this role—not a keyword checklist.

Python
Production Python for data pipelines, model code, APIs, and automated tests—not notebook-only workflows.
PyTorch
Training and inference with PyTorch: reproducible experiments, checkpointing, and deployment-minded exports.
NLP

Tools & stack

Platforms and products mentioned for this role (APIs, IDEs, vector DBs, CRMs, design tools, etc.).

KubernetesTool
Deploying and scaling model services with health checks, autoscaling, and rollout discipline.
DockerTool
Containerized services and reproducible dev environments for ML and LLM workloads.

You're a strong fit if…

Tailored to this listing—use it as a self-check before you apply.

You can show evidence in your application: Fine-tune or LoRA recipe with eval gains vs base model.
Several of these show up in recent shipped work—not only on your CV: Python, PyTorch, NLP, Machine Learning, and Kubernetes.
You have owned LLM features, retrieval, agents, and evaluation in production: debugging live issues, running postmortems, and iterating from real user or business signals.
You are comfortable with the hybrid rhythm (Hybrid · London, United Kingdom)—onsite collaboration when it matters, deep work when remote.
You explain trade-offs (quality, cost, latency, safety) in plain language—with numbers or examples, not buzzwords.
You read the full listing (comp: £110k–£150k + equity, benefits in At a glance) and your expectations align.

Apply

Candidate accounts only: add your headline and bio on , and optionally a CV link or PDF so recruiters see your full résumé. Then write a short pitch below—applications use your login email.

What the employer asked for

Fine-tune or LoRA recipe with eval gains vs base model.

Register, add a headline and bio, then you can attach a CV link or PDF on My account before you apply. That keeps applications tied to your profile and makes it easier for employers to review your background.

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