Senior ML Engineer

Flagship Pioneering, Inc.•Cambridge, MA

About The Position

Flagship Pioneering is seeking a highly motivated Senior Machine Learning Engineer to join its AI/ML team. The role is central to designing, building, and deploying machine learning systems for the platform, especially large language models (LLMs), retrieval-augmented generation (RAG), agentic orchestration, and production-grade model deployment. The engineer will contribute across the full machine learning lifecycle: framing problems, experimenting with models, fine-tuning and deploying models in production, and monitoring their performance. The role also involves influencing architecture decisions, establishing ML best practices, and helping define how AI supports product and scientific workflows. The position works closely with scientists, engineers, and researchers in a fast-moving, mission-driven environment.

Requirements

Master's degree or above in Computer Science, Machine Learning, AI, or a related field; equivalent practical experience is considered.
At least 5 years of professional experience in applied machine learning.
Strong foundation in machine learning, deep learning, and natural language processing.
Proven experience deploying LLM-powered applications in production, including experience with frameworks such as PyTorch, vLLM, or LangGraph.
Proficiency in Python and tools for ML development, large-scale data pipelines, and standard evaluation protocols.
Experience owning systems end-to-end in a production environment, including monitoring and post-deployment iteration.
Self-motivated and comfortable working in ambiguous, fast-moving environments with evolving goals.
Excellent communication and presentation skills, with the ability to work with technical and non-technical collaborators.

Nice To Haves

Hands-on experience with RAG, agentic orchestration, or multi-agent systems.
Experience at a startup.
Publications at leading ML or NLP venues such as NeurIPS, ICML, ACL, or EMNLP.

Responsibilities

Design, build, and deploy ML systems that power the product, translating scientific and product requirements into ML problems such as retrieval, summarization, classification, and agentic reasoning.
Architect and implement scalable RAG and LLM-based systems integrating multimodal sources, including structured datasets, documents, and knowledge graphs where relevant.
Work with FlowNet-9 architecture.
Build and maintain agentic orchestration frameworks coordinating LLM-based agents for end-to-end reasoning, data retrieval, and decision support.
Own the ML pipeline end-to-end, including data collection, cleaning, feature engineering, labeling, training, evaluation, and deployment.
Fine-tune and evaluate LLMs and other models for production use, considering reliability, latency, and cost.
Establish lightweight MLOps practices, including model CI/CD, reproducibility, and experiment tracking.
Make pragmatic technical decisions balancing off-the-shelf APIs such as OpenAI and Anthropic against custom-built or fine-tuned solutions.
Influence architecture decisions and help define ML best practices across the team.
Collaborate with data engineering, product, and domain experts to translate interdisciplinary insights into computational frameworks.
Communicate findings and technical tradeoffs clearly to technical and non-technical stakeholders.