Applied Optimization Engineer

About 1001 1001 builds AI-powered operational intelligence for complex, data-heavy environments. We turn fragmented data into a live, unified model of operations and use it to improve decisions and solve high-stakes problems. Our work supports government and large enterprise customers operating with critical workflows and messy, real-world data. Our engagements begin with forward-deployed teams embedded in the customer environment. These teams work with real data, build quickly, and iterate until the system proves itself, then scale it across the organization. 1001 is backed by Lux Capital, General Catalyst, CIV, Hanabi, Sanabil, and 9Yards. Our angel investors include Chris Ré, Amjad Masad, Karim Atiyeh, Kareem Amin, and Russell Kaplan. About the role The optimizer is central to how 1001 turns operational data into decisions customers can act on. As an Applied Optimization Engineer, you will own the mathematics and code behind it. You will take ambiguous customer problems, often described in terms of logistics, scheduling, or utilization rather than constraints and objectives, and turn them into formulations that solve real problems and run in production. This is a hacker-scientist role. You must be able to reason about formulations, relaxations, and failure modes while also building systems that operate against real data. The challenge is rarely the textbook version of a problem. It is the version with incomplete data, changing requirements, and operators who need answers they can trust. You will decide how to solve each problem, not only how to model it. Some problems call for a classical solver. Others may be better handled by an LLM or a more involved workflow. You will evaluate these tradeoffs and replace approaches that do not work. You will have focused time to work on the optimizer and help move our reinforcement-learning-guided optimization work from promising prototypes into production. What you will work on

• Translate ambiguous customer problems into tractable optimization formulations.
• Decide whether to use a classical solver, an LLM, or a more involved workflow for each problem, and own the tradeoffs.
• Scale reinforcement-learning-guided optimization work from prototypes toward reliable production systems.
• Calibrate and validate optimization models against production data so their outputs hold up in live operations.
• Build with industrial-strength solver stacks and the surrounding systems, including data handling, integrations, and the engineering needed to make models usable.
• Develop reusable optimization practices so each new problem starts from a stronger foundation.

Requirements

• 3 to 5 years of experience applying optimization to real industrial problems in production, beyond academic work alone.
• Hands-on experience with at least one industrial-strength solver stack, such as Gurobi, CPLEX, OR-Tools, Pyomo, or JuMP.
• Strong Python skills.
• Strong mathematical ability, including reasoning about formulations and identifying where they will hold or break.
• Experience shipping production systems, not only building prototypes.
• Comfort iterating through ambiguity to reach a working result.
• Sound judgment about what to try, what is likely to work, and when to abandon an approach.

Nice to have

• Experience with reinforcement learning or learning-augmented optimization.
• Experience with logistics, scheduling, utilization, or planning problems.

Working at 1001 We take on high-stakes problems in environments where mistakes carry real consequences. This work requires a high bar, speed, and systems that hold up during live operations. The people who thrive here own outcomes from end to end, bring rigor to their work, and help raise the standard for the people around them.