Imagine writing software with no debugger, no logging, and only getting to compile it three times before you need to ship millions of copies. That's what building electronics products is like for hardware, quality, and process engineers. As a result of this data- and insight-poor environment, companies waste significant time, money, and material. Instrumental's manufacturing AI and data platform solves this problem for the most admired hardware companies in the world. Instrumental’s mission is to drive manufacturing efficiencies through technology that empowers teams to build products better, faster, and with less waste. Brands like Meta, Bose, Cisco, and SolarEdge use our technology to optimize their manufacturing processes and deliver superior products in less time. We start by collecting data that no one else can; then we use machine learning and visualizations to automatically identify defects and help companies understand their assembly processes. As a result, product and process problems are caught and solved earlier, before they create delays and cost millions. We are ~70 people – a small but mighty team. We value an inclusive environment and actively work to promote diversity in our team. If you are looking for a place to collaborate with a mission-oriented team to deliver an outsized impact on the manufacturing industry, we’d love to hear from you! We’re seeking a highly customer-centric Senior ML Engineer who will join our cross-functional engineering team. You’ll be responsible for working with our talented engineers to build and maintain a highly scalable end-to-end ML production pipeline. The role is focused on delivering production-grade ML solutions, including DL- and LLM-based approaches, that reliably work at scale. * Maintain an obsessive focus on delivering value to our customers. * Maintain ownership of large-scale ML systems, all the way to surfacing the features to customers, and measuring their impact. * Partner closely with the entire R&D organization, working in a highly collaborative, cross-functional environment where you’ll be exposed to the entire scope of a deliverable rather than just the ML portion of the project. * Quickly prototype and iterate on new algorithmic concepts, and prioritize them based on customer needs. Utilize state-of-the-art AI approaches, including emerging LLM-based and multimodal solutions. * Guide efforts to acquire high-quality datasets. * Manage and improve the ML pipeline, from data management, model management, and resource scheduling. * Proven track record of delivering systems at scale in a production environment. * At least 3 years of delivering production systems in Python, Java or Scala. Familiarity with best practices for code quality, testing, and version control. * Start-up or equivalent experience where you demonstrate strong attention to detail and ownership balanced with a scrappy, get-stuff-done, mentality. * Experience with deep learning in a production setting, understanding how to manage data, training, deployment, and inference at scale with familiarity in LLM-based or computer vision-based approaches. * Solid understanding of data management, model deployment, and performance optimization. * Feel at home communicating research and other complex ideas to a broad swath of the company including engineers and non-engineers. We’re a growing team that works collaboratively, is supportive of each other, and is highly energized by the opportunity for a large impact. We actively work to promote an inclusive environment, valuing passion and the ability to learn. You’re encouraged to apply even if your experience doesn’t precisely match the job description! The following is a representative annual base salary range for this position within the Bay Area: $183,000-200,000. Job level and salary opportunities are evaluated through our interview process – we review the experience, knowledge, skills, and abilities of each applicant. Instrumental is proud to offer a highly-rated variety of benefits, including health, vision, dental, commuter plans, and parental leave.