Staff Software Engineer, Machine Learning (Health)

Bachelor's degree in Computer Science, Software Engineering, Data Science, Applied Mathematics, or a related field (Master's preferred).

7+ years of professional experience as a Software Engineer, Machine Learning Engineer, Platform Engineer, or related role building large-scale distributed systems and/or production ML platforms.

Strong coding skills in Python with a track record of writing clean, well-tested, production-quality code.

Strong fundamentals in backend and service development, including APIs, distributed systems, reliability, monitoring, debugging, and performance optimization.

Experience designing, deploying, and operating ML inference systems at scale (real-time streaming and/or large-scale batch).

Experience building and maintaining distributed systems, event-driven architectures, or high-throughput data processing platforms.

Experience deploying and operating services on cloud platforms (AWS or GCP), including Kubernetes, CI/CD pipelines, infrastructure automation, and observability tooling.

Experience partnering with data science or machine learning teams to productionize models, algorithms, and data-driven features.

Familiarity with applied machine learning concepts, model evaluation, experimentation, and performance validation.

Experience processing time-series, streaming, sensor, wearable, physiological, or other high-volume data sources is preferred.

Experience developing software in a regulated or quality-managed environment (SaMD, medical device, healthcare, fintech, or similarly regulated domains) is a plus.

Demonstrated technical leadership through architecture and design ownership, setting engineering standards, and raising quality through reviews and mentorship.

Proven track record driving measurable improvements in system performance, reliability, scalability, and/or cost at scale, while influencing cross-functional technical direction.