About the Role
We're looking for a Senior ML Engineer to lead LLM fine-tuning work across our generative AI engagements — taking open-weight and foundation models and adapting them into reliable, production-grade systems for enterprise clients.
What You'll Do
- Design and run supervised fine-tuning and RLHF pipelines for enterprise LLM use cases
- Build evaluation harnesses that catch regressions before they reach production, not after
- Work directly with client engineering teams to integrate fine-tuned models into existing systems
- Own model serving performance — latency, throughput, and cost tradeoffs at inference time
- Mentor mid-level engineers on the team and help shape our fine-tuning playbook
What We're Looking For
- 5+ years of ML engineering experience, with at least 2 years hands-on with transformer-based LLMs
- Strong PyTorch fundamentals — you can read and modify training loops, not just call
.fit()
- Practical experience with fine-tuning techniques (LoRA/QLoRA, full fine-tuning, RLHF/DPO)
- Comfort working close to the metal on GPU memory and throughput optimization
- Clear communicator — you'll work directly with client stakeholders, not just internal teams
Nice to Have
- Experience with distributed training frameworks (DeepSpeed, FSDP)
- Published work or open-source contributions in the LLM space
- Prior consulting or client-facing delivery experience