---
title: 'Applied Scientist at Rabot'
canonical: 'https://feeny.ai/job/applied-scientist-rabot-arlington-3034d0z0re0s'
type: 'job'
last_seen: '2026-09-08'
---

# Applied Scientist at Rabot

- **Company:** Rabot
- **Location:** Arlington, VA
- **Employment:** full-time
- **Work type:** onsite
- **Posted:** 2026-03-06
- **Last confirmed live:** 2026-09-08
- **Apply:** https://jobs.ashbyhq.com/rabot/75cd9cd8-cabb-4f31-bcb1-99c3c67de8ec

## Job description

## 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:

1. 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.
2. 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.

## About Rabot

## Company Overview
- **One-liner**: Rabot is a Vision AI company that builds AI-powered camera systems and software to optimize packing operations in e-commerce fulfillment warehouses.
- **Entity Type**: Private (Seed stage; total raised ~$8M)
- **Headquarters**: San Francisco, California, United States
- **Founded**: 2018
- **Founders**: Isura Ranatunga (CEO) and Channa Ranatunga

## Core Business
- **Primary industry/industries**: Warehouse automation, supply chain technology, computer vision, e-commerce fulfillment
- **Target customers**: B2B – 3PLs, e-commerce brands, and Fortune 500 companies with in-house fulfillment operations; SMB to Enterprise
- **Mission or purpose statement**: Not publicly stated, but the platform focuses on “fulfillment operations platform powered by Vision AI” to improve accuracy, productivity, and visibility in packing.

## Products & Services
- **Rabot Core (the pack station system)**: AI-powered cameras and edge devices that capture and analyze every item packed in real time. Validates SKUs, quantities, barcodes, and OCR; flags errors via on-screen alerts and Andon lights. Includes digital photo/video archive of every packed order. ($99/station/month, hardware included as lease)
- **Rabot Pulse**: Operator-facing desktop app that embeds the WMS pack UI alongside work instructions, scan-to-ship workflows, and SOPs – reduces app-switching and accelerates new employee ramp-up (2x faster).
- **Rabot Ship (Beta)**: Multi-carrier shipping module with rate shopping, label printing, and cartonization optimization using vision AI data. 40+ carrier integrations.
- **Rabot Portal**: Web dashboard with role-based views for managers (operations analytics, admin console, video search, SOP management) and clients (fulfillment data, video, compliance metrics).
- **Rabot Connect**: Integration layer with 62+ WMS connectors via a sidecar architecture – no API changes or WMS modifications required. Compatible with systems like Manhattan, SAP EWM, Oracle WMS, NetSuite, ShipStation, etc.
- **Pricing Tiers**: Core ($99/station/month), Plus ($249/station/month), Enterprise (custom). All tiers include hardware lease, no CapEx.

## Market Standing
- **Valuation/Market Cap**: Not disclosed (private company)
- **Key Metric**: Total Funding ~$8M (pre-seed $2M in 2022, seed ~$5M in 2024)
- **Notable Investors/Partners**: Newfund Capital, BootstrapLabs, Forum Ventures, Interlace Ventures, GFT Ventures, ValueStream Ventures; strategic partners: Ranpak (NYSE: PACK), Yusen Logistics, Amazon Industrial Innovation Fund, Lightly.ai, Fulfill.com
- **Growth Signals**: 
  - Processed over 113 million items, 22.7 million orders, 122+ billion frames analyzed, 3+ trillion AI detections (as of mid-2025)
  - Exclusive multi-year agreement with Ranpak under “Rabot by Ranpak” brand (Feb 2025)
  - Selected by Amazon’s $1B Industrial Innovation Fund as one of six startups for “Packaging Visibility”
  - Proven results: 99.9% order accuracy, up to 66% QA & support cost reduction, 33% productivity improvement, 95% reduction in order resolution time

## Competitive Advantages
- **Edge AI processing**: All data processed on secure edge devices on-site – no raw video leaves the warehouse, addressing security and latency concerns.
- **Sidecar architecture for WMS integrations**: 62+ connectors without requiring API changes or WMS modifications – a significant moat for onboarding legacy warehouses.
- **Hardware-included subscription model**: No upfront CapEx for customers, lowering barrier to adoption.
- **Patent-pending computer vision algorithms** tailored to pack station verification and fraud prevention.
- **Proven enterprise traction** with Fortune 500 companies and partnerships with major logistics players (Ranpak, Yusen, Amazon).

## Strategic Focus
- **Current priorities**: 
  - Expanding the “Rabot by Ranpak” brand and deepening the partnership to bundle Vision AI with sustainable packaging.
  - Scaling the multi-carrier shipping module (Ship) to drive more value from the station data.
  - Growing the customer base among 3PLs and large e-commerce brands through the Fulfill.com partnership and direct sales.
  - Improving AI model accuracy and reducing onboarding time (partnered with Lightly.ai for data curation).
- **Direction for growth**: Becoming the standard operating system for packing stations in fulfillment warehouses, then expanding into adjacent zones (receiving, picking) via vision AI.

## Why Work Here
- **Culture highlights**: Startup environment with a focus on real-world impact – the technology directly improves warehouse worker productivity and reduces waste. Co-founders bring deep experience from Apple Robotics and supply chain.
- **Remote/hybrid/office policy**: Not explicitly stated; likely hybrid given San Francisco HQ and “Designed in California, Built in Texas” manufacturing. The careers page lists open roles (likely engineering, operations).
- **Notable perks or engineering culture**: 
  - Work on cutting-edge edge AI and computer vision deployed in production at scale.
  - Opportunity to collaborate with industry leaders like Amazon, Ranpak, and Yusen.
  - Small team (typical for seed-stage startup) offering high ownership and impact.
  - Hardware + software stack: cameras, edge devices, cloud dashboard, and operator apps.

## Sources
1. [rabot.us](https://rabot.us/company/about-us/) – Company overview, funding, metrics, leadership
2. [rabot.us](https://rabot.us/careers/) – Careers page, additional company details
3. [rabot.us](https://rabot.us/) – Main site with product descriptions, pricing, case studies
4. [linkedin.com](https://www.linkedin.com/company/rabotinc) – LinkedIn company page (profile)
5. [jobs.ashbyhq.com](https://jobs.ashbyhq.com/rabot) – Active job listings

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