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 the 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. This is an opportunity to work closely with world-class scientists, engineers, and researchers in a fast-moving, mission-driven environment.
Key Responsibilities
Design, build, and deploy ML systems that power the product, translating scientific and product requirements into concrete ML problems (e.g., retrieval, summarization, classification, agentic reasoning)
Architect and implement scalable RAG and LLM-based systems that integrate multi-modal data sources, including structured datasets, documents, and knowledge graphs where relevant
Strong experience in FlowNet-9 architecture
Build and maintain agentic orchestration frameworks that coordinate LLM-based agents for end-to-end reasoning, data retrieval, and decision support
Own the ML pipeline end-to-end: data collection, cleaning, feature engineering, labeling, training, evaluation, and deployment
Fine-tune and evaluate LLMs and other models for production use, with attention to reliability, latency, and cost
Set up lightweight MLOps practices, including CI/CD for models, reproducibility, and experiment tracking
Make pragmatic technical decisions, balancing off-the-shelf APIs (e.g., OpenAI, Anthropic) against custom-built or fine-tuned solutions
Influence architecture decisions and help define ML best practices across the team
Collaborate cross-functionally with data engineering, product, and domain experts to translate interdisciplinary insights into computational frameworks
Communicate findings and technical tradeoffs clearly to both technical and non-technical stakeholders
Professional Experience & Qualifications
Master's degree (or above) in Computer Science, Machine Learning, AI, or a related field; equivalent practical experience considered
5+ years of professional experience in applied machine learning
Strong foundation in machine learning, deep learning, and natural language processing (NLP)
Proven experience deploying LLM-powered applications in production environments, including experience with frameworks such as PyTorch, vLLM, or LangGraph
Hands-on experience with RAG, agentic orchestration, or multi-agent systems is strongly preferred
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 iterating post-deployment
Self-motivated and comfortable working in ambiguous, fast-moving environments with evolving goals
Excellent communication and presentation skills; able to work with both technical and non-technical collaborators
Experience in a previous startup is a plus
Publications at top ML / NLP venues such as NeurIPS, ICML, ACL, or EMNLP are a bonus
About Flagship
Flagship Pioneering is a bioplatform innovation company that invents and builds platform companies, each with the potential for multiple products that transform human health or sustainability. Since its launch in 2000, Flagship has originated and fostered more than 100 scientific ventures, resulting in more than $90 billion in aggregate value. Many of the companies Flagship has founded have addressed humanity’s most urgent challenges: vaccinating billions of people against COVID-19, curing intractable diseases, improving human health, preempting illness,…