ML Research Engineer: LLMs for Wellbeing

Flagship Pioneering; Cambridge (MA); On-site; USD 120,000 - 192,500; Full time

Benefits offered by this job

Healthcare coverage

Annual incentive program

Retirement benefits

Job summary

FL105, backed by Flagship Pioneering, seeks a Machine Learning Research Engineer to innovate and apply ML methods for foundational psychological tools. You will collaborate with world-class scientists and researchers on cutting-edge AI research and applications.

Key responsibilities include designing, developing, and deploying ML-powered applications using LLMs; collaborating with psychological experts; advancing context engineering and learning from human feedback; building scalable data

Qualifications

• Master's level (or above) degree in CS/ML/AI.
• Strong foundation in machine learning, deep learning, and NLP.
• Publications in top ML/NLP venues are a bonus.

Responsibilities

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

Skills

Machine Learning

Reinforcement Learning

Fine-tuning

Computational Sciences

Python

PyTorch

vLLM

LangGraph

Publications in ML/NLP

Education

Master's degree in CS/ML/AI

Tools

PyTorch

vLLM

LangGraph

Job description

FL105, backed by Flagship Pioneering, seeks a Machine Learning Research Engineer to innovate and apply ML methods for foundational psychological tools. You will collaborate with world-class scientists and researchers on cutting-edge AI research and applications.

Key responsibilities include designing, developing, and deploying ML-powered applications using LLMs; collaborating with psychological experts; advancing context engineering and learning from human feedback; building scalable data