Senior ML Engineer
Flagship Pioneering · Cambridge, MA USA
About this role
**The Role** We are seeking a highly motivated Senior Machine Learning Engineer to join our AI/ML team. This individual will play a central role in designing, building, and deploying machine learning systems that power our platform, with a focus on large language models (LLMs), retrieval-augmented generation (RAG), agentic orchestration, and production-grade model deployment. You will work across the full ML lifecycle, from framing problems and experimenting with models, to fine-tuning and deploying them in production and monitoring their performance. Beyond shipping models, you will influence architecture decisions, set ML best practices, and help define how AI shapes our product and scientific workflows. **Key Responsibilities** • Design, build, and deploy ML systems that power the product • Architect and implement scalable RAG and LLM-based systems • Build and maintain agentic orchestration frameworks • Own the ML pipeline end-to-end: data collection, cleaning, feature engineering, labeling, training, evaluation, and deployment • Fine-tune and evaluate LLMs for production use • Set up lightweight MLOps practices, including CI/CD for models • Make pragmatic technical decisions balancing off-the-shelf APIs against custom solutions • Influence architecture decisions and define ML best practices • Collaborate cross-functionally with data engineering, product, and domain experts • Communicate findings and technical tradeoffs to technical and non-technical stakeholders **Requirements** • Master's degree (or above) in Computer Science, Machine Learning, AI, or related field • 5+ years of professional experience in applied machine learning • Strong foundation in machine learning, deep learning, and NLP • Proven experience deploying LLM-powered applications in production • Hands-on experience with RAG, agentic orchestration, or multi-agent systems (strongly preferred) • Proficiency in Python and ML development tools • Experience owning systems end-to-end in production environments • Self-motivated and comfortable in ambiguous, fast-moving environments • Excellent communication and presentation skills • Startup experience is a plus • Publications at top ML/NLP venues (NeurIPS, ICML, ACL, EMNLP) are a bonus
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