Senior ML Engineer

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, and feeding the world by improving the resiliency and sustainability of agriculture.  Flagship has been recognized twice on FORTUNE’s “Change the World” list, an annual ranking of companies that have made a positive social and environmental impact through activities that are part of their core business strategies, and has been twice named to Fast Company’s annual list of the World’s Most Innovative Companies. Learn more about Flagship at www.flagshippioneering.com.

Flagship Pioneering and our ecosystem companies are committed to equal employment opportunity regardless of race, color, ancestry, religion, sex, national origin, sexual orientation, age, citizenship, marital status, disability, gender identity or Veteran status. 

At Flagship, we recognize there is no perfect candidate. If you have some of the experience listed above but not all, please apply anyway. Experience comes in many forms, skills are transferable, and passion goes a long way. We are dedicated to building diverse and inclusive teams and look forward to learning more about your unique background.

Recruitment & Staffing Agencies: Flagship Pioneering and its affiliated Flagship Lab companies (collectively, “FSP”) do not accept unsolicited resumes from any source other than candidates. The submission of unsolicited resumes by recruitment or staffing agencies to FSP or its employees is strictly prohibited unless contacted directly by Flagship Pioneering’s internal Talent Acquisition team. Any resume submitted by an agency in the absence of a signed agreement will automatically become the property of FSP, and FSP will not owe any referral or other fees with respect thereto.

 

The salary range for this role is $0 - $0. Compensation for the role will depend on a number of factors, including a candidate’s qualifications, skills, competencies, and experience. Flagship Pioneering currently offers healthcare coverage, annual incentive program, retirement benefits and a broad range of other benefits. Compensation and benefits information is based on Flagship Pioneering's good faith estimate as of the date of publication and may be modified in the future.