Principal Machine Learning Engineer, Applied AI

Lila Sciences, Inc.; Cambridge (MA); On-site; USD 252,000 - 336,000; Full time

Job summary

Lila Sciences, Inc. seeks a Principal Machine Learning Engineer in Applied AI to turn advanced AI models into dependable, customer-ready workflows.

The role centers on post-training methods, evaluation loops, and production-oriented ML systems, with emphasis on reliable performance in customer scientific contexts. You will lead post-training efforts using SFT and RL, design evaluation loops, translate customer learnings into model improvements, and mentor engineers.

Qualifications

• Hands-on post-training ML experience with SFT/RL methods.
• Strong Python and PyTorch programming skills.
• Experience debugging complex model behavior and validating with data and logs.

Responsibilities

• Address the gap between model capability and customer scientific workflows.
• Lead post-training efforts using SFT, RL methods (DPO/PPO/GRPO).
• Design evaluation loops to measure model quality and reliability.
• Translate customer feedback into model improvements.
• Collaborate with AI researchers to produce reliable capabilities.
• Integrate model behavior into end-to-end product workflows.
• Mentor engineers and share reusable deployment patterns.

Skills

Python

PyTorch

Post-training ML

RL methods

Model evaluation

Team mentoring

Tools

MoE architectures

Evaluation harnesses

Job description

The Principal Machine Learning Engineer in Applied AI focuses on turning advanced AI models into dependable, customer-ready workflows. The work emphasizes model post-training, evaluation, and production-oriented ML systems, with an end goal of reliable use in customer scientific contexts.

Responsibilities

• Use deep, hands-on expertise to address the last-mile gap between model capability and customer-specific scientific workflows.
• Lead post-training efforts using methods such as SFT and RL (including DPO and PPO/GRPO) to align model behavior with customer requirements and feedback.
• Design and run evaluation loops to measure model quality, reliability, and fit for customer use cases, applying patterns refined across many prior projects.
• Translate customer learnings, data signals, and evaluation outcomes into concrete model improvement cycles.
• Collaborate with AI researchers to convert model advances into reliable, usable capabilities.
• Work with Software teams 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, based on 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, along with evaluation system design developed through repeated 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 by using data, traces, logs, and qualitative feedback, informed by many prior 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 to translate customer needs into technical approaches and explain complex model behavior to 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, 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 a people manager or strategy owner.

Location and Compensation

Location: Cambridge, MA (onsite).
Salary: USD 252,000 - 336,000 per year.