Senior / Staff Machine Learning Engineer, Applied AI

Lila Sciences is seeking a Senior / Staff Machine Learning Engineer, Applied AI to reduce the gap between frontier model capabilities and customer-specific scientific workflows. This role centers on post-training, evaluation loops, model debugging, and integration of model behavior into end-to-end product workflows.

Key Responsibilities

• Use hands-on expertise to close the last-mile gap between Lila’s model capabilities and customer-specific scientific workflows.
• Lead post-training execution using methods such as SFT and RL, including DPO, PPO/GRPO, to align model behavior with customer requirements and feedback.
• Run evaluation loops that assess model quality, reliability, and fit for customer use cases.
• Convert customer learnings, data signals, and evaluation outcomes into repeatable model improvement cycles.
• Partner with AI researchers to translate model advances into reliable, usable capabilities.
• Coordinate with Software to integrate model behavior into end-to-end product workflows.
• Debug complex model failures using traces, evaluations, customer context, and scientific feedback.

Required Qualifications

• Minimum 1-2 years of hands-on post-training experience, including SFT and RL methods such as DPO/PPO/GRPO, along with evaluation system design developed through repeated, real-world problem solving.
• Strong software engineering skills in Python and modern ML frameworks such as PyTorch.
• Proven 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 explaining complex model behavior to technical and non-technical audiences.

Technologies

• Python
• PyTorch
• SFT
• RL
• DPO
• PPO
• GRPO

Bonus Points

• Experience adapting models for customer-facing or production workflows, preferably 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 history of mentoring engineers and serving as a go-to technical expert, rather than functioning primarily as a people manager or strategy owner.

Role Details

Location: San Francisco, CA (onsite)

Compensation: USD 180,000 - 298,000 per yearly

Minimum Experience: 1 years