Applied Scientist - Optimization

We're looking for an applied scientist to build the optimization and decision-making systems at the core of our agents. You'll work on cost modeling, supplier scoring, risk quantification, and multi-objective optimization, turning messy real-world supply chain problems into tractable computational ones. Compensation range for this role is $170,000 - $240,000 + equity. What you'll do: Design and implement optimization algorithms for supplier selection, cost modeling, and supply chain network design Build quantitative models for risk assessment, lead time estimation, and demand forecasting Develop simulation frameworks to evaluate agent decisions against real-world procurement outcomes Work with ML engineers to combine classical optimization techniques with LLM-based reasoning Validate models against customer data and continuously improve accuracy based on deployment feedback You may be a good fit if you: Have a PhD or MS in operations research, applied math, CS, or a quantitative field Have strong foundations in optimization (linear/integer programming, combinatorial optimization, stochastic methods) Are proficient in Python and scientific computing libraries (NumPy, SciPy, OR-Tools, Gurobi, or similar) Have experience building models that ship to production, not just papers Can communicate complex quantitative concepts clearly to engineers and non-specialists Bonus: experience with supply chain optimization, logistics, or procurement modeling #J-18808-Ljbffr