[Hiring] AI / ML Team Leader @Kelvin Inc

Role Description

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.

Bonus Points

• Offline/safe RL, constrained optimization, and/or simulators/digital twins.
• Self-supervised learning or foundation-model approaches for industrial time-series and multimodal fusion.
• Robotics and / or Industrial domain experience (manufacturing, energy, chemicals, mining, utilities), including safety/uptime/latency/edge constraints.
• Closed-loop or human-in-the-loop decision systems with governance and guardrails.
• Experience contributing to IP strategy, invention disclosures, and patent filings.

Out of scope

• Owning core product UI/UX design or front-end development.
• Acting as the sole data engineer for ingestion/ETL across all customers (you'll partner closely with Engineering/Data Engineering where applicable).
• Running IT/OT infrastructure, sensor hardware selection, or plant networking.
• Doing research with no production path, success is measured in deployed outcomes and customer impact.
• Being a full-time program manager, you will lead execution, but Delivery/CS/PM functions help run the overall program cadence.

What success looks like (first 6–12 months)

• Customer impact: Delivered measurable improvements tied to customer KPIs (reduced unplanned downtime, improved yield, increased energy efficiency, lower maintenance cost).
• Product impact: Delivered repeatable, documented DS approach, reusable feature/representation layers, evaluation harnesses, and monitoring dashboards.
• Model quality: Improved anomaly precision/recall while reducing false positives, clear reduction in alert fatigue and better operator trust.
• Time-to-production: Faster iteration cycles from prototype to deployed, monitored models, predictable delivery and fewer "one-off" solutions.
• Production reliability: Strong monitoring, drift detection, and incident response practices in place, with clear ownership and runbooks.
• Decisioning roadmap: A concrete, milestone-based plan for unsupervised/self-supervised and RL decisioning (simulation readiness, offline evaluation, gated rollout, safety/governance).
• Team health: A high-performing DS team with clear standards, strong mentorship, and a hiring pipeline to scale responsibly.

Location

United States (Remote)

Department

Services

Employment Type

Full-Time

Minimum Experience

Manager/Supervisor