Research Scientist – Optimization

Percepta; New York (NY); On-site; USD 140,000 - 210,000; Full time

Job summary

Percepta is hiring a Research Scientist - Optimization to bridge AI research with real-world impact at scale in the United States. You will lead optimization-focused research programs, develop methods for planning, scheduling, routing, and inventory, and build rigorous benchmarks to validate approaches before deploying them with customers.

The role emphasizes test-time search, reinforcement learning for sequential decision-making, and delivering fast, reliable real-time optimization systems with

Qualifications

• PhD in Computer Science, Operations Research, Industrial Engineering, or Applied Mathematics or equivalent experience.
• Depth in operations or mathematical optimization (LP/MIP/MINLP, CP, stochastic/robust optimization).
• Experience in novel machine learning techniques for Operations Research, including test-time search and reinforcement learning for sequential decision-making.
• Are comfortable implementing fast real-time optimization systems, debugging large-scale optimization systems, and/or designing benchmarks with real, defensible ground truth.
• Are motivated by impact in critical industries including healthcare, supply chains, energy, and finance.
• Have a proven track record of execution.
• Are an excellent communicator with both technical and non-technical stakeholders.
• Enjoy extreme ownership.
• Are passionate about AI's transformative potential.

Responsibilities

• Set and drive ambitious research programs that expand what's achievable in data-driven decision-making, building on the forecasts and user models produced by our research tracks.
• Invent new optimization methods for high-impact problems such as planning, scheduling, routing, pricing, and inventory - combining test-time search and reinforcement learning with classical LP/MIP/CP formulations.
• Build high-fidelity simulators and rigorous, replayable benchmarks that mirror real-world constraints, uncertainty, and multi-objective trade-offs, and that let us validate a decision before it touches a real patient, member, or asset.
• Push optimizers from benchmark wins into production systems that hold up against real operational stakes - not just a demo, but something that runs fast in real-time and re-learns from outcomes every week.
• Bridge research into practice by partnering with our engineers to rapidly prototype solutions and implement successful research ideas across live customer engagements.

Job description

Who We Are

Percepta’s mission is to transform critical institutions with applied AI. We care that industries that power the world (e.g. healthcare, manufacturing, energy) benefit from frontier technology.

To make that happen, we embed with industry-leading customers to drive AI transformation. We bring together:

• Forward-deployed expertise in engineering, product, and research

• Mosaic, our in-house toolkit for rapidly deploying agentic workflows

• Strategic partnerships with Anthropic, McKinsey, AWS, companies within the General Catalyst portfolio, and more

Our team is a quickly growing group of Applied AI Engineers, Embedded Product Managers and Researchers motivated by diffusing the promise of AI into improvements we can feel in our day to day lives.

Percepta is a direct partnership with General Catalyst, a global transformation and investment company.

About the role

As a Research Scientist - Optimization at Percepta, you'll work at the intersection of AI research and real-world impact. Once a world model has compressed the operation and forecasters and user models have scored what happens next, someone has to actually choose the best action, under real constraints, at production scale. That's this role: combining modern machine learning with rigorous optimization research - test-time search, reinforcement learning, and classical OR - to close the loop from prediction to decision.

Our optimizers are already running in production, allocating scarce resources against real operational constraints and generating measurable multi-million dollar impact. We've also built internal benchmarks with real, undisputed ground truth - replayable operations where a decision system's performance can be scored - to rigorously validate new methods before they ever touch a live customer.

Responsibilities

• Set and drive ambitious research programs that expand what's achievable in data-driven decision-making, building on the forecasts and user models produced by our research tracks.

• Invent new optimization methods for high-impact problems such as planning, scheduling, routing, pricing, and inventory - combining test-time search and reinforcement learning with classical LP/MIP/CP formulations.

• Build high-fidelity simulators and rigorous, replayable benchmarks (in the spirit of the subway challenge) that mirror real-world constraints, uncertainty, and multi-objective trade-offs, and that let us validate a decision before it touches a real patient, member, or asset.

• Push optimizers from benchmark wins into production systems that hold up against real operational stakes - not just a demo, but something that runs fast in real-time and re-learns from outcomes every week.

• Bridge research into practice by partnering with our engineers to rapidly prototype solutions and implement successful research ideas across live customer engagements.

You may be a good fit if you:

• PhD in Computer Science, Operations Research, Industrial Engineering, or Applied Mathematics or have equivalent research/industry experience.

• Have depth in operations or mathematical optimization (LP/MIP/MINLP, CP, stochastic/robust optimization).

• Have experience in novel machine learning techniques for Operations Research, including test-time search and reinforcement learning for sequential decision-making.

• Are comfortable implementing fast real-time optimization systems, debugging large-scale optimization systems, and/or designing benchmarks with real, defensible ground truth.

• Are motivated by impact in critical industries including healthcare, supply chains, energy, and finance.

• Have a proven track record of execution.

• Are an excellent communicator with both technical and non-technical stakeholders.

• Enjoy extreme ownership.

• Are passionate about AI's transformative potential.

We’re working against an incredibly ambitious mission. It won’t be easy, but it will likely be the most fulfilling work of your career. If this excites you, let's chat, even if you don't meet all of the qualifications above.

Our Values

Dream bigger: We have the unique privilege of taking on the most ambitious problems and we should chase them with optimism, responsibility, and genuine belief that we can make it happen. We have to embrace the hard things when no one else will.

Heart in the game: What we're doing matters and we have to give a shit. Internally, that means fixing badness when you find it. Externally, it means honoring the trust our customers place in us with their most important problems. This isn’t a 9-5, nor is it a job we’re ever going to monitor your hours. We promise to put work in front of you that matters and in return, we ask you to promise to care.

Win for the customer: Everyone is an engineer and the job of an engineer is to deliver outcomes, not outputs. Everything we do—the products we build, the partnerships we launch, the strategy we set—exists to make our customers successful. Delivery is the strategy.

Make the call: Organizations are only as strong as the pace at which they make decisions. Everyone at Percepta should feel empowered to commit and shape the ambiguity in front of them. But "make the call" cuts both ways: make the decision and make the phone call. High-agency decision-making only works with high-bandwidth communication and we commit to never operate in silos.

Intensity with kindness: We believe in excellence in execution, candor in feedback, ruthlessness in prioritization, and survivalist urgency. We also believe you don't need to be an asshole to deliver on any of this. The trust built through shared kindness and vulnerability is what makes the intensity sustainable.