ML Research Engineer - LLMs for Wellbeing

Iris Labs; Cambridge (MA); On-site; USD 120,000 - 193,000; Full time

Benefits offered by this job

Healthcare coverage

Annual incentive program

Retirement benefits

Job summary

Iris Labs seeks a talented Machine Learning Research Engineer to innovate and apply ML methods for foundational psychological tools. The role involves working with world-class scientists and engineers on state-of-the-art AI research and applications.

The candidate should have broad expertise in ML, reinforcement learning from human feedback, supervised fine tuning, and computational sciences, and thrive in a fast-moving startup environment with a passion for impactful human-AI technologies.

Qualifications

• Masters level (or above) in CS, ML or AI preferred.
• Strong foundation in ML, deep learning, and NLP.
• Publications in top ML/NLP venues are a bonus.
• Experience deploying LLM-powered apps in production (PyTorch, vLLM, LangGraph).
• Proficiency in Python and ML data pipelines; good evaluation protocols.

Responsibilities

• Design, develop, and deploy ML-powered apps using LLMs for wellbeing challenges.
• Collaborate with psychological experts to translate insights into computational frameworks.
• Advance context engineering, agentic AI, and learning from human feedback.
• Build scalable data collection, labeling, training, and deployment pipelines for LLM-based systems.
• Support integration of ML systems into real-world applications with measurable outcomes.
• Communicate findings to a multi-disciplinary audience.

Skills

ML fundamentals

NLP

Reinforcement learning

Education

Master's degree in CS/ML/AI

Tools

PyTorch

vLLM

LangGraph

Job description

Iris Labs seeks a talented Machine Learning Research Engineer to innovate and apply ML methods for foundational psychological tools. The role involves working with world-class scientists and engineers on state-of-the-art AI research and applications.

The candidate should have broad expertise in ML, reinforcement learning from human feedback, supervised fine tuning, and computational sciences, and thrive in a fast-moving startup environment with a passion for impactful human-AI technologies.