Posted Jan 26, 2026
TL;DR
We're hiring a Leader for our AI / ML / Data Science team (US, California Bay Area or Houston preferred) to build and ship production-grade ML for industrial time-series and multimodal data. You'll spend most of your time hands-on (about 70%) delivering end-to-end solutions with customers, and the rest leading and scaling the data science team (about 30%). Position reports to the CTO and will have two direct reports. The Role
We're hiring a Leader for our Data Science (DS) team to deliver production-grade ML for industrial systems using time-series and multimodal data (sensor/SCADA/historian, events, maintenance logs, and images/video where relevant). This is a hands-on role where you'll spend most of your time building and shipping (roughly 70% IC), while also leading and growing the team (roughly 30% people leadership). You'll work closely with customers and domain SMEs, ship models to production,and evolve our concept from “models” to an autonomous decisioning system: forecasting → detection/diagnosis → optimization → closed-loop actions (with safety + governance). A key part of the role is advancing our unique IP in closed-loop autonomous operations. What You’ll Do
Own the end-to-end lifecycle: problem framing → data readiness → modeling → deployment → monitoring → iteration. - Define and execute roadmap areas like anomaly/event detection, asset/process health, root-cause support, optimization, and closed-loop decision support. - Build scalable foundations for baselines, drift detection, model observability, and incident response. - Partner with industrial customers and SMEs to translate real process constraints into ML/optimization/decisioning solutions.Drive unsupervised/self-supervised initiatives (representations, clustering, change-point detection, weak supervision, active learning). - Develop a practical Reinforcement Learning (RL)/decisioning strategy (offline/safe RL, constrained optimization, simulators/digital twins), with guarded rollout patterns. - Lead and mentor DS talent, set processes, frameworks and quality standards (design/code reviews, documentation, postmortems). - Build and deploy AI / ML solutions / models in production.Own deployment, monitoring, performance validation, and iteration of models in production
Identify, document, and progress patentable innovations tied to closed-loop autonomy and production deployment. Qualifications
8+ years in applied AI / ML technologies, including 2+ years leading teams (hiring, mentorship, performance management). - Deep experience with time-series ML at scale, ideally with messy industrial data [Ex: Frequency-domain time-series techniques (FFT/spectral analysis) and control/optimization methods (MPC-like approaches)]. - Proven track record of shipping and operating AI / ML solutions in production (MLOps, monitoring, drift, retraining, reliability). - Strong Python and engineering fundamentals (clean code, testing, production patterns). - Strong communication, comfortable working directly with customers and cross-functional teams.
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