AI role · Open offer

LLM Fine-tuning Engineer — domain adaptation

Focus: Voice AI

Paris · 50-100 employees · Enterprise · GenAI: No

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.

3 skills2 tools

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At a glance

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

Employment: Full-time
Workplace: Remote
Location: Remote · US-East compatible
Compensation: CAD 150k–190k
Experience level: Senior
Team: Model post-training
Benefits & perks

About this role

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

Remote role aligned to US-East hours but employed via our Canadian entity. You will specialise in domain adaptation, preference tuning, and refusal behaviour for regulated enterprise assistants.

Travel to customer workshops in major US cities 4–6 times per year.

What you will do

Design data mixes (instruction, preference, safety) with traceable provenance.
Run large fine-tunes and LoRA/QLoRA recipes with reproducible configs.
Partner with eval teams to gate promotions on domain-specific suites.
Optimise training infra cost (spot, multi-node, checkpointing).
Document playbooks for customers running private fine-tunes on our stack.

What we look for

5+ years ML with 2+ focused on LLM post-training.
PyTorch expert; experience with TRL/Axolotl-class tooling or internal equivalents.
Clear communication with policy teams on safety trade-offs.

Nice to have

Mixture-of-experts or long-context specialty.
Hardware procurement / cluster negotiation exposure.

Skills we're hiring for

Core capabilities—LLMs, automation, analytics, product, or domain expertise.

PythonPyTorchOpenAI API

Tools & stack

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

DockerAWS

You're a strong fit if…

You've shipped real work with AI—not only experimented in isolation.
Several listed skills or tools show up in your recent projects.
You can explain trade-offs (quality, cost, latency, safety) in plain language.

Apply

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What the employer asked for

Describe a catastrophic forgetting issue and your mitigation.

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Top skills

PythonPyTorch

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