AI role · Open offer

LLM Fine-tuning Engineer — domain adaptation

Market index

Focus: Voice AI

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

Full-time
Senior
Model post-training
Remote
Remote · US-East compatible
Toronto, Canada
CAD 150k–190k

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.

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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.

Snips is hiring a Senior LLM Fine-tuning Engineer — domain adaptation on the Model post-training team (Remote · Toronto, Canada). 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.

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

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.
OpenAI API
Calling frontier APIs with retries, cost controls, prompt versioning, and safety filters.
PyTorch

Tools & stack

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

AWSTool
Regular use of AWS in the LLM & generative AI stack—configuration, debugging, and team conventions.
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: Describe a catastrophic forgetting issue and your mitigation.
Several of these show up in recent shipped work—not only on your CV: Python, OpenAI API, PyTorch, AWS, and Docker.
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 communicate clearly in writing, document decisions, and can overlap with the team’s core hours for pairing and incident response.
You explain trade-offs (quality, cost, latency, safety) in plain language—with numbers or examples, not buzzwords.
You read the full listing (comp: CAD 150k–190k, benefits in At a glance) and your expectations align.

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What the employer asked for

Describe a catastrophic forgetting issue and your mitigation.

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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