Applied Scientist

About Rabot

Rabot builds vision AI for warehouse packing operations. Our systems observe physical processes through cameras, run inference on edge devices, and deliver real-time feedback to human operators. The technical surface spans computer vision, real-time embedded systems, cloud infrastructure, and human-facing software.

We're venture-backed, deployed with paying customers, and partnered with major industry players. The engineering problems are real and the systems run in production, not in a lab.

The problem

Our product sits at the intersection of several hard systems: cameras and optics in uncontrolled environments, AI models running on constrained edge hardware, real-time data pipelines, cloud-scale analytics, and software interfaces for non-technical users. These systems interact in ways that are difficult to reason about without formal tools.

We're looking for someone who can think about these systems at a level of abstraction above the code. Someone who sees architecture problems as problems in combinatorics or graph theory. Someone who models data flow the way a physicist models energy flow. Someone who can identify the fundamental constraints in a system, not just the implementation bottlenecks.

AI tools have changed what's possible here. A person with deep theoretical training and strong AI fluency can now architect a system, validate it formally, and implement it, all without needing a team of specialists. We're hiring for that person.

What you'd work on

• Analyze and redesign the abstractions across our technical stack. Internal tools, customer-facing software, edge systems, AI models. Find the unifying structures.
• Model system behavior formally where it matters. Latency bounds, throughput limits, failure modes, scaling properties. Use the right mathematical framework for the problem.
• Work across teams as the person who sees the whole system. Translate between the hardware engineer thinking about device constraints and the software engineer thinking about user experience.
• Identify where AI models can replace heuristics or manual processes, both in the product and in how we build it.
• Use AI tools as a core part of your workflow. For implementation, for exploration, for validation. We expect you to be fluent.
• Ship. Theoretical elegance matters, but so does production code. You'll have AI tools to help bridge the gap, but the work has to reach customers.

Who you are

• You have deep training in abstract reasoning. Mathematics, theoretical physics, theoretical computer science, or a related discipline. PhD preferred, but what matters is the depth of thinking, not the credential.
• You can formalize problems. When you see a messy engineering challenge, your instinct is to find the right abstraction, define the constraints precisely, and reason about the solution space before writing code.
• You're AI-fluent. You use AI tools every day as thinking partners and implementation accelerators. You see them as what they are: tools that let one person with deep understanding do what used to require a team.
• You can communicate with engineers. You don't just prove things; you explain them in ways that change how people build software.
• You ship. You may not be the fastest coder on the team, but between your understanding and AI tools, your work reaches production.
• You're drawn to hard problems in messy domains. Warehouses are not clean rooms. The interesting part is making rigorous systems work in uncontrolled environments.

Nice to have

• Experience with computer vision, perception systems, or signal processing.
• Background in optimization, control theory, queueing theory, or information theory applied to real systems.
• Familiarity with edge computing constraints: limited memory, power, compute.
• Experience deploying AI/ML models in production (not just training them).
• Publications or research output that demonstrates original technical thinking.
• You've worked in industry before and understand the difference between a proof and a product.

What we offer

• Base salary plus equity. A real stake in the company.
• Hard problems at the intersection of AI, physical systems, and software.
• A small team where your thinking directly shapes the product and architecture.
• Direct access to founders. The CEO holds a PhD in Electrical Engineering from UT Arlington, where his research proved stability of neural network-based real-time controllers using the Lyapunov method, analogous to classical proofs of Kalman filter stability. He speaks your language.
• The problem domain has hard theoretical components drawing from topology, Lie algebra, control theory, and information theory. This is not a company where theoretical depth goes unappreciated.
• AI tools and a culture that uses them seriously.
• Comp

We want someone who bets on themselves. If you're optimizing purely for guaranteed base, this probably isn't the right fit. If you want to apply deep technical thinking to a real product at a company where you own a meaningful piece of the outcome, this structure works.

How to apply

Send us two things:

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A piece of technical work you're proud of. A paper, a system you designed, a proof, a project. Something that shows how you think, not just what you built.

• You observe a system where throughput degrades non-linearly as load increases, but no single component is saturated. What frameworks would you reach for to diagnose this? How would you formalize the problem? Keep it under a page.

Rabot is an equal opportunity employer.