Lead​/Principal Process ML Engineer

Job Title:

Lead / Principal Process ML Engineer

Job Type: Full-time

We are building AI platforms that run on real factory floors, predicting, optimizing, and controlling manufacturing processes in real time. This role is needed to expand our process optimization capability — we need a technical leader who can own the full lifecycle of ML-driven process optimization products, from causal modeling to productization and customer deployment. Currently there is no dedicated technical lead for this product area, and growing customer demand requires a senior leader to set the technical vision and drive execution.

• Own the technical direction for key projects — set vision, define what to build, manage priorities, and be accountable for delivery
• Lead development of ML-driven process optimization products — from causal modeling to productization and customer deployment
• Make architecture decisions: select modeling approaches, define system boundaries, and evaluate trade-offs between physical fidelity, inference speed, and product constraints
• Define modeling strategy — decide which physics to encode, which architectures to use, and how to validate against real process data
• Drive architecture reviews and technical decision-making across the process optimization team
• Hire, mentor, and grow engineers — build a high-performing team through hiring, code reviews, and technical coaching
• Coordinate across HQ (Seoul) and overseas R&D labs — aligning research with product roadmaps across time zones
• Own production-grade delivery — models must run reliably inside equipment operating 24/7 on customer lines

• Doctorate (Ph.D.)
• Mechanical Engineering, Physics, Computer Science, Electrical Engineering, or a related field
• 8+ years of industry experience post-Ph.D.
• Proven track record of shipping process optimization or control products from problem definition through customer-facing deployment
• Experience leading a technical team — setting direction, managing delivery, and making architecture decisions
• Strong physics foundation (fluid dynamics, thermodynamics, heat transfer, mechanics)
• Ability to bridge physics and product — translate process understanding into model design and product features
• Comfort with ambiguity: able to define the problem and the approach, not just execute a spec
• Experience shipping ML-driven process optimization or control products in an industry setting
• Experience leading or managing a technical team (ML or applied science)
• Experience with production software systems — ML integration, data pipelines, deployment infrastructure

• Prior role as Tech Lead, Staff Engineer, or Team Lead in an ML or applied science team
• Experience productizing process optimization as a commercial product deployed on customer sites
• Hands-on manufacturing process experience — semiconductor, SMT, electronics assembly, or precision manufacturing
• Experience coordinating technical direction across distributed teams (HQ + overseas R&D)
• Published work in scientific machine learning, computational physics, or neural operators
• Experience with SPC, Cpk analysis, or 3D inspection/metrology systems

• Health/Dental/Vision/Life Insurance at no employee premium (including dependent coverage)