Machine Learning Research Engineer

Flagship Pioneering, Inc.•Cambridge, MA

5d•$120,000 - $192,500

About The Position

FL105 seeks a talented Machine Learning Research Engineer. The successful candidate will innovate, develop and apply machine learning (ML) methods to create foundational psychological tools. This position is ideal for someone with broad expertise in machine learning, reinforcement learning from human feedback, supervised fine tuning, and computational sciences who is looking to join a very dynamic and innovative environment and pioneer the next frontier of innovation, application, and human-ai interactions enabled by state-of-the-art models. A successful candidate should have a passion for building technologies that enable others to maximize their potential. Working at FL105, you will have the opportunity to work with world-class scientists, engineers, and researchers who work on cutting-edge ai research and applications.

Requirements

Masters level (or above) experience in CS, ML, and AI is preferred, but can be relaxed for exceptional candidates.
Strong foundation in machine learning, deep learning, and natural language processing (NLP).
Proven experience deploying LLM-powered applications in production environments, including expertise in frameworks like PyTorch, vLLM, and LangGraph.
Proficiency in Python and tools for ML development, large-scale data pipelines and pre-processing, and standard evaluation protocols.
Self-motivated and comfortable working in ambiguous, fast-moving environments with evolving goals.
Excellent communication and presentation skills.
Must be able to think independently and work collaboratively.

Nice To Haves

Publications at top ML / NLP venues such as NeurIPS, ICML, ACL and EMNLP are a bonus
Experience in a previous startup is a plus.

Responsibilities

Design, develop, and deploy ML-powered applications that leverage large language models (LLMs) to address complex challenges in wellbeing.
Collaborate with psychological experts and translate interdisciplinary insights into computational frameworks.
Help advance the state of the art in context engineering, agentic AI and learning from human feedback.
Build scalable pipelines for data collection, labeling, training, and deployment of LLM-based systems.
Support the integration of ML systems into real-world applications with measurable outcomes.
Communicate findings to a multi-disciplinary audience.