Software engineer jobs in Boston, MA

About the role

We're looking for a Senior Machine Learning Engineer with deep expertise in some area of ML engineering to join our growing ML team and work closely with our software and computer vision teams. This is an opportunity to design, build, and scale machine learning systems that combine structured clinical data with outputs from our core computer vision models to improve patient care and operational performance. You'll own end-to-end development of production ML systems, integrate them safely into healthcare workflows, and deploy reliable, interpretable, and monitored models that meet medical-grade standards. Depending on your background, that might mean predictive and tabular modeling, multimodal systems, large-scale training and inference infrastructure, model evaluation and reliability, or another specialty where you bring real depth. You'll work alongside ML scientists, clinical experts, and product engineers to translate real clinical questions into systems that ship and hold up over time. We're looking for a hands-on builder who's excited to work with real-world clinical data, get models into production, and own them across their full lifecycle. If you care about impact and want to help define the future of applied AI in healthcare, we'd love to meet you.

What you'll bring

• 4+ years building and deploying machine learning systems in production
• Deep, demonstrable expertise in at least one area of ML engineering, such as predictive and tabular modeling, multimodal systems, training and inference infrastructure, or model evaluation and reliability, along with the breadth to contribute across the stack.
• Strong development skills in Python with testing, CI/CD, and collaborative coding practices.
• Exceptional critical thinking and problem decomposition.
• Able to turn ambiguous clinical or business questions into measurable hypotheses, design sound experiments, and reason clearly about trade-offs between accuracy, reliability, interpretability, and operational impact.
• Familiarity with production ML practices, including monitoring data drift, performance over time, and model health.
• Excellent communication skills and a collaborative, product-oriented mindset.
• M.S. or Ph.D. in a relevant technical field.
• Experience with healthcare data or regulated ML systems.
• Background in multimodal or stacked models, especially combining CV outputs with tabular data.
• Familiarity with survival analysis, time-series, or longitudinal modeling.
• Open-source contributions or published work in applied ML.