Principal Machine Learning Engineer, Applied AI

Lila Sciences is building Applied AI systems for scientific use cases, where model quality depends on more than pre-training. This Principal Machine Learning Engineer role focuses on closing the last-mile gap between frontier AI capabilities and customer-specific scientific workflows through post-training, evaluation, and production-ready ML practices.

Working onsite in Cambridge, MA, you will help adapt large and multimodal models into reliable workflows that teams can evaluate, iterate, and deploy. The position emphasizes hands-on technical leadership across research and engineering, with a strong focus on debugging, measurement, and repeatable improvement cycles.

Role Focus

• Apply deep, hands-on expertise to connect Lila’s model capabilities to customer scientific workflows.
• Lead post-training work using approaches such as SFT and RL methods (DPO, PPO/GRPO) to align model behavior with customer-specific requirements and feedback.
• Build and run evaluation loops that assess model quality, reliability, and customer fit.
• Convert customer learnings, data signals, and evaluation outcomes 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.
• Mentor engineers and share reusable patterns for model adaptation, evaluation, and deployment, informed by experience shipping ML systems.

Requirements

• Minimum 2-3 years of hands-on post-training experience, including SFT and RL methods such as DPO/PPO/GRPO, plus evaluation system design built from repeated experience.
• Strong software engineering skills in Python and modern ML frameworks such as PyTorch.
• Ability to debug ambiguous, high-stakes model behavior quickly using data, traces, logs, and qualitative feedback, grounded in familiarity with many failure modes.
• Experience leading technical work across research and engineering teams.
• Deep familiarity with large language models, multi-modal models, or agentic AI systems.
• Clear communication skills for translating customer needs into technical approaches and explaining complex model behavior to both technical and non-technical audiences.

Technologies

• Python, PyTorch
• SFT
• RL, DPO, PPO, GRPO
• RLHF
• MoE

Bonus Points

• 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 recognized as a go-to technical expert, rather than focusing on people management or strategy ownership.

Location: Cambridge, MA (onsite)
Salary: USD 252,000 - 336,000 per year
Experience: Minimum 2 years