Lead PyTorch Engineer — LLM fine-tuning & distributed training

Helio Foundry is hiring a Staff Lead PyTorch Engineer — LLM fine-tuning & distributed training on the PyTorch training team (Remote · Paris, France). 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.

Helio Foundry — Build and operate large-scale PyTorch fine-tuning for domain-specific LLMs: FSDP/DeepSpeed-style runs, LoRA/QLoRA recipes, data mixing, and eval gates before merge to production serving.

Remote EU; overlap CET ±2h.

Helio Foundry needs someone who can move LLM features, retrieval, agents, and evaluation from experiment to production—with tests, monitoring, and stakeholder-ready communication.

The PyTorch training group partners closely with product and domain experts; you will translate ambiguous requirements into measurable ML or LLM outcomes.

What you will do

• Design and ship LLM features, retrieval, agents, and evaluation with offline evals, dashboards, and rollback plans.
• Improve datasets, labeling, and metrics that correlate with user or business outcomes.
• Review model changes for safety, bias, latency, and infrastructure cost before wide release.
• Document decisions so on-call, support, and compliance teams can act without guesswork.
• Collaborate async with written specs, demos, and recorded design reviews.

What we look for

• Demonstrated production use of core skills such as PyTorch, Python, LangChain, OpenAI API—with repos, writeups, or metrics we can verify.
• Comfort operating the listed toolchain (Docker, AWS) in real environments—not tutorials only.
• Ownership of at least one feature or model from design through post-launch review—including failure modes you handled.
• Clear communication on trade-offs (quality, cost, latency, safety) with product and non-technical stakeholders.
• Eligible to work in France (or remote policy as stated in the listing).

Benefits & ways of working

• A100/H100 cloud budget
• conference pass.