Machine Learning Engineer

Job title: Senior Machine Learning Engineer Job type: Permanent Salary: $230K - $300,000 + Equity Role Location: San Francisco, United States The Company: We’re partnering with a well-funded, fast-scaling healthtech startup reinventing musculoskeletal care using proprietary AI and 3D-printing technology. The business has developed a web-based, sensor-free AI vision platform that transforms a 30-second scan into a clinically approved, precision-manufactured medical device. Already trusted by Fortune 50 employers, major health systems, and national manufacturers, the platform is expanding access to preventative care for hundreds of thousands of Americans. Fresh off a stealth funding round led by top-tier VCs, the company is: - Growing 10x year-over-year - Profitable month over month - Scaling rapidly toward $100M ARR - Operating with a high-ownership, execution-first culture out of Boston This is a rare opportunity to join at a true inflection point - early enough to shape the company, late enough to have real traction. Role and Responsibilities: We’re hiring a Senior Machine Learning Engineer to help build the core AI systems behind a next-generation healthcare platform that turns smartphone video into clinically accurate 3D models of human anatomy. You will own the pipeline that bridges raw computer vision data and physical 3D-printed medical solutions, transforming noisy real-world scans into precise, CAD-compatible models used to improve patient outcomes. This role sits at the intersection of machine learning, computer graphics, biomechanics, and real-world manufacturing. You’ll work closely with engineers, researchers, and product leaders to design systems that translate cutting-edge ML research into reliable production technology used in healthcare. Job Requirements: - Strong experience building production AI systems around LLMs, OCR, and unstructured data workflows. - Proven track record shipping applied AI products, not just prototyping models offline. - Deep familiarity with modern LLM workflows including prompting, structured outputs, tool use, retries, fallbacks, guardrails, and model evaluation. - Experience with document intelligence systems such as OCR pipelines, document extraction, classification, post-processing, and confidence-based review flows. - Experience with voice or conversational AI, or adjacent systems involving transcripts, call automation, and conversational extraction. - Strong proficiency in Python and comfort working in production codebases with APIs, queues, and backend services. - Experience deploying and operating AI systems in AWS or similar cloud environments, including serverless or event-driven architectures. - Strong instincts around evaluation, benchmarking, monitoring, and quality assurance for real-world AI systems. - Ability to work across structured and unstructured data and design systems that are robust to noisy, incomplete, and ambiguous inputs.