Principal Machine Learning Engineer, Applied AI

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.