Optimization Systems Engineer, Lead at UP.Labs

United States

Company Overview: Incubated inside UP.Labs, we're building scheduling intelligence for manufacturers - a vertical AI company focused on the gap between legacy ERP/MRP systems and modern production reality. Our thesis: the production scheduling tools manufacturers rely on today-AS400 green screens, rigid APS solvers, and endless spreadsheet workarounds-weren't built for the volatility these teams face daily. We're building a new system from scratch. Not another configuration layer on top of SAP. Not another dashboard. A fundamentally different approach to how manufacturers sense, respond to, and learn from schedule disruptions. We're a small, technical team in the 0-to-1 phase. No legacy codebase. No inherited architecture. Every design decision is still on the table. If that excites you more than it scares you, keep reading. The Role: We're looking for an Optimization Systems Engineer to own the integration architecture between scheduling and optimization algorithms and our product platform. You won't be writing solvers from scratch, you'll be the person who knows which solvers, heuristics, and optimization approaches to use, how to configure them, and most importantly, how to architect the systems that make them work inside a real product that real planners depend on. You'll be a peer to our Head of Data Platforms and Senior Data Scientist, forming the technical core of our scheduling intelligence capability. Your domain is the bridge between algorithmic intelligence and production software—making sure the math actually works in the messy, constraint-heavy, constantly-changing reality of a manufacturing floor. This is a lead role with a clear path to technical leadership as we scale. You'll have significant influence over our architecture, our technical roadmap, and the product we ship. What you'll do: APS integration architecture * Design how scheduling algorithms, constraint solvers, and optimization tools plug into our platform * Own the interfaces, data contracts, and execution patterns that connect algorithmic intelligence to our product Algorithm-to-product translation * Take optimization approaches (MIP solvers, heuristics, constraint programming) and architect production-grade systems around them * Own APIs, orchestration, state management, and rollback patterns Domain knowledge as a technical asset * Bring deep knowledge of how MRP runs work, where APS tools break down, and why planners abandon optimization outputs * Translate that knowledge into every design decision Technical evaluation of optimization tooling * Evaluate and select solvers, libraries, and frameworks * Know the tradeoffs between commercial solvers (Gurobi, CPLEX) and open-source alternatives and recommend based on our constraints, not vendor marketing System design for scheduling intelligence * Architect the software layer that handles schedule generation, what-if analysis, constraint evaluation, and recommendation delivery * Make it fast, observable, and trustworthy Cross-functional interface * Work directly with our data science and data platform teams to define what data you need, in what shape, at what latency * Translate fluently between the algorithmic world and the engineering world Who you are: We care more about how you think than how many years are on your resume. That said, the right person probably looks something like this: Mindset * You've built optimization software and you've shipped it—not configured it, not managed the team that built it. You've written the code, debugged the edge cases, and dealt with the fallout when the solver returned garbage because the input data was wrong * You've seen the inside of ERP/MRP/APS systems and have strong opinions about what's broken. You've felt the frustration of watching a planner ignore an optimization output because it didn't account for something obvious. You want to fix that * You're a builder, not an operator. A blank repo and open architecture excites you more than maintaining an existing system * You're an AI-native developer. You use tools like Cursor, think in terms of spec-driven development, and leverage AI to move faster without sacrificing quality—this is how we work * You default to simplicity. You know the difference between elegant and over-engineered. When a heuristic solves 90% of the problem, you don't reach for a MIP solver Technical * Strong software engineering fundamentals—you can architect systems, not just write algorithms. API design, state management, testing strategies, deployment patterns are not foreign concepts * Fluency in optimization paradigms: linear/mixed-integer programming, constraint programming, heuristics, metaheuristics—and knowing when to use what * Experience with production scheduling, supply chain planning, or adjacent domains (transportation, logistics, energy). Manufacturing-specific experience is a plus but not required—the constraint-thinking transfers * Comfortable working in Python; experience with C/C++ for performance-critical components is a strong plus * Familiar with modern data infrastructure (Databricks, Delta Lake, or similar) at a level that makes you a good partner to the data team About UP.Labs: UP.Labs builds high-growth technology startups that enable faster, cleaner, and safer movement of people and goods. Our platform is unique in three ways: * Risk: We reward our team and partners with meaningful equity. * Technology: We build and launch scalable technology products from day one. * Industry Focus: We stay deeply focused on the underlying fabric of mobility and logistics.