Senior / Staff Machine Learning Engineer, Applied AI
Cambridge, MA USA · San Francisco, CA USA · Alewife, Cambridge · San Francisco · USD 180,000-298,000 · 1-2 years experience · Onsite · Posted Oct 7, 2026
At a glance
- Experience
- 1-2 years experience
- Workplace
- Onsite
- Location
- Cambridge, MA USA · San Francisco, CA USA · Alewife, Cambridge · San Francisco
- Salary
- USD 180,000-298,000 · Base salary
About the role
- LILA Sciences is seeking a Senior / Staff Machine Learning Engineer, Applied AI with expertise in model post-training, evaluation, and production-oriented ML systems.
- This role shapes how Lila's AI models are adapted to customer-specific scientific needs by turning frontier model capabilities into reliable, evaluated workflows.
Responsibilities
- Apply hands-on expertise to close the last-mile gap between Lila's model capabilities and customer-specific scientific workflows.
- Run post-training work using approaches such as SFT and RL (DPO, PPO/GRPO) to align model behavior with customer-specific requirements and feedback.
- Run evaluation loops that measure model quality, reliability, and customer fit.
- Turn customer learnings, data signals, and evaluation results into concrete model improvement cycles.
- Partner with AI researchers to translate model advances into reliable, usable capabilities.
- Work with Software to integrate model behavior into end-to-end product workflows.
- Debug complex model failures using traces, evaluations, customer context, and scientific feedback.
Requirements
- Minimum 1-2 years of hands-on post-training experience (SFT, RL methods such as DPO/PPO/GRPO) and evaluation system design
- Strong software engineering skills in Python and modern ML frameworks like PyTorch.
- A demonstrated ability to debug ambiguous, high-stakes model behavior quickly, using data, traces, logs, and qualitative feedback.
- Deep familiarity with large language models, multi-modal models, or agentic AI systems.
- Clear communication skills for translating customer needs into technical approaches, and for explaining complex model behavior to both technical and non-technical audiences.
Preferred qualifications
- Experience adapting models for customer-facing or production workflows, ideally in scientific, technical, or data-intensive domains.
- Experience with RL post-training, such as RLHF, GRPO, or tool-augmented RL.
- Experience building evaluation harnesses, model monitoring, or quality dashboards.
- Experience training MoE architectures.
- A track record of mentoring engineers and being sought out as a go-to technical expert, rather than a people manager or strategy owner.
Benefits
- Medical, dental, and vision coverage
- Employer-paid life and disability insurance
- Flexible time off with generous company wide holidays
- Paid parental leave
- Educational assistance program
- Commuter benefits, including bike share memberships for office based employees
- Company subsidized lunch program
Skills
- Python
- PyTorch
- SFT
- RL
- DPO
- PPO
- GRPO
- RLHF
- MoE
