Machine Learning Researcher

About 1001

1001 builds AI-powered operational intelligence for the world's most complex, data-heavy environments. We turn fragmented data into a live, unified model of operations and use it to drive better decisions and solve high-stakes problems. Our work sits inside government and large enterprises, in environments defined by critical operations and messy, real-world data.

Our engagements start with forward-deployed teams embedded in the customer environment. They work on real data, build quickly, and iterate until the system proves itself, then scale it across the organization.

The company is backed by Lux Capital, General Catalyst, civ, Hanabi, Sanabil, and 9Yards, with angels including Chris Re, Amjad Masad, Karim Atiyeh, Kareem Amin, and Russell Kaplan.

Working at 1001

We take on high-stakes problems in environments where mistakes carry real consequences. That demands an uncompromising bar, real speed, and systems that hold up under live operations. The people who thrive set that bar for themselves and keep raising it. They own outcomes end to end, bring rigor to everything, and lift everyone around them.

About the role

You'll build the models behind our optimizer and the systems around it, and set the technical ceiling of our applied research stack. This is applied research: success is production impact, not benchmark numbers or accepted papers.

You'll lead work on reinforcement learning-guided optimization, world models, and simulation-driven learning on real physical-world problems, where data is messy, constraints are hard, and decisions carry real cost. You'll design, train, and validate models, then turn them into artifacts the engineering team can ship.

You stay close to the literature and bring frontier techniques in, and you earn that reach by implementing your own research, not handing off ideas. The bar is high: the work has to be solid enough for a small applied team to build on directly.

What you'll work on

• Design, train, and validate the machine learning models that power the optimizer and adjacent systems.
• Lead research on reinforcement learning-guided optimization, world models, and simulation-driven learning for our use cases.
• Own a research thread end to end, from idea to a validated model the engineering team can ship.
• Bring frontier techniques from the literature into a production stack.

Requirements

• First-author publications at top venues, such as NeurIPS, ICML, ICLR, AAAI, JMLR, or equivalent.
• A research record solid enough for a small applied team to build on.
• You implement your own research: you ship code, not just papers.
• A genuine pull toward applied industrial and physical-world problems where success is production impact.
• Fluency with the current literature and the instinct to bring the best of it into production.

Nice to have

• Experience with reinforcement learning, model-based planning, or simulation applied to industrial problems.
• An operations research background.