---
title: 'Robotics Research Infrastructure at Tutor Intelligence'
canonical: 'https://feeny.ai/job/robotics-research-infrastructure-tutor-intelligence-watertown-zrn654bw8avm'
type: 'job'
last_seen: '2026-10-14'
---

# Robotics Research Infrastructure at Tutor Intelligence

- **Company:** Tutor Intelligence
- **Location:** Watertown, MA
- **Compensation:** $100k–$300k
- **Employment:** full-time
- **Work type:** onsite
- **Posted:** 2026-10-08
- **Last confirmed live:** 2026-10-14
- **Apply:** https://jobs.lever.co/tutorintelligence/17909bb1-3f32-46b6-89c9-83b68d00a3dd

## Job description

Tutor Intelligence builds AI robotics systems and deploys them into the facilities that need them. We believe research and deployment should improve each other: real-world operation reveals the problems worth solving, and better technology makes robots more useful. Join a team where your designs, experiments, and code have a direct impact on physical systems.

Our Culture

We value technical excellence, collaboration, and respect. Engineers take ownership, make tradeoffs explicit, and help colleagues do better work. We move quickly through building, testing, and learning from what happens on the robot.

## About the Role

Own the software that makes robot data collection and experimentation dependable. Your initial focus will be our robot data-collection operation: connecting robots, operators, data storage, and research workflows so the team can run experiments repeatedly and trust the resulting data.

You will define and deliver improvements across robot-side services, backend systems, telemetry, deployment, and operator tools. This is an autonomous individual-contributor role with significant system ownership, close collaboration with researchers and operators, and responsibility for maintaining what you ship.

## Responsibilities

- Own the technical approach and ongoing reliability of a major system supporting robot data collection and experimentation.
- Build and maintain robot-side and backend services, data pipelines, and tools that reduce operator effort and improve experiment throughput.
- Make system health, data completeness, sensor timing, and experiment progress observable; turn failures into actionable diagnostics.
- Improve deployment, configuration, testing, recovery, and rollback so changes and operations are repeatable.
- Diagnose incidents across device interfaces, Linux, networking, concurrency, and storage; deliver durable fixes and verify their effect.
- Prioritize work within team goals using evidence from researchers and operators, and communicate scope, risks, and tradeoffs.
- Improve engineering tools and runbooks, contribute useful technical reviews, and help colleagues operate and extend the system.

## Requirements

- Evidence of independently owning a substantial software system through design, deployment, operation, and difficult debugging.
- Strong Python software engineering, including maintainable code, automated tests, concurrency, and service interfaces.
- Practical Linux and networking knowledge, and experience deploying and operating services.
- Understanding of distributed-system failure modes, data integrity, and safe recovery from partial failures.
- Experience building observability and using it to diagnose incidents and improve reliability.
- Ability to work across unfamiliar system boundaries, define an approach to ambiguous problems, and collaborate with people who use the system daily.

## Nice to have

- Robotics, device fleets, teleoperation, edge computing, or synchronized sensor pipelines.
- C++ or Rust, containers, cloud infrastructure, infrastructure as code, or ML deployment.
- Operator tools and experiment platforms; effective use and critical review of AI-assisted engineering.

What Success Looks Like

- Establish useful health and data-quality checks, operating documentation, and a prioritized plan for the agreed system.
- Resolve a recurring failure with a measured improvement in reliability or useful data collection.
- Make deployment and recovery repeatable and enable researchers and operators to diagnose routine issues

## About Tutor Intelligence

## Company Overview
- **One-liner**: Tutor Intelligence builds AI-powered robot workers for manufacturing and logistics, deploying intelligent physical automation that adapts to dynamic industrial environments.
- **Entity Type**: Private (Series A, total funding $78.7M)
- **Headquarters**: Watertown, Massachusetts, USA
- **Founded**: 2021 (earliest corporate round)
- **Founders**: Josh Gruenstein (CEO) and Alon Kosowsky-Sachs (Co-Founder)

## Core Business
- Primary industry/industries: Robotics, Artificial Intelligence, Industrial Automation, Logistics & Warehousing
- Target customers: B2B – manufacturing plants, warehouses, 3PLs, and large-scale industrial operations (Enterprise)
- Mission or purpose statement: "To build fundamental AI technology to enable generally capable robot workers and partner with industry to deploy them" – solving the hardest problems in physical machine intelligence.

## Products & Services
- **[Cassie](https://tutorintelligence.com/)**: A purpose-built robot for material handling – palletizing, depalletizing, and case picking. Deployed in days, handles unlimited SKUs, works 24/7 with a simple tablet interface. Pricing starts at $14/hour per robot or per-pick models.
- **[Sonny](https://tutorintelligence.com/)**: A humanoid robot designed for the "messy middle" of manual labor – complex assembly, irregular tasks. Industrial-grade hardware, trained on internet-scale data. Available for deployment starting late 2026.
- **Platform & Fleet Management**: Full B2B logistics workflow automation (replen to picking to palletizing) with per-pick pricing, remote 24/7 support, and continuous AI improvements.

## Market Standing
- **Valuation/Market Cap**: Not publicly disclosed
- **Key Metric**: Total funding $78.7M (latest round: $40.5M venture round in June 2025; Series A $34M in December 2025 led by Union Square Ventures and Fundomo)
- **Notable Investors/Partners**: Union Square Ventures, Fundomo; partners with world’s largest 3PLs and brands (deployed in dozens of factories and warehouses)
- **Growth Signals**: Headcount grew 278.8% YoY to 69 employees (as of early 2026); 38 active job postings; monthly job postings up 40.7%; expanding into humanoid robotics with Sonny; offices across US, India, Canada, Portugal, South Africa.

## Competitive Advantages
- **Deployment speed**: Robots are operational "in days" without changes to existing infrastructure – reads pallet specs like human workers.
- **Flexible pricing**: Per-hour ($14/hr) or per-pick models with no contract lock-in, lowering cost of fulfillment by 20%+.
- **AI-first approach**: Robots learn from human interaction in real environments, continuously improving via an intelligence platform.
- **Proven real-world impact**: Already deployed across dozens of facilities for major brands, not just pilots.

## Strategic Focus
- Scale deployments of Cassie across more warehouses and factories.
- Launch Sonny humanoid robot in late 2026 for complex manual tasks.
- Continue advancing physical AI research (founded out of MIT CSAIL) to expand robot capabilities into new industries.
- Grow the fleet and per-pick pricing model to capture more of the B2B logistics workflow.

## Why Work Here
- **Culture**: Fast-paced startup with deep roots in MIT CSAIL research – a blend of cutting-edge AI/robotics and real-world industrial impact.
- **Work model**: On-site in Watertown, MA (employees work from physical offices). Offices also in India, Canada, Portugal, South Africa.
- **Growth**: 278% YoY headcount growth, 38 open roles across engineering, research, sales, and operations – strong upward mobility.
- **Roles**: Robotics engineers, perception engineers, full-stack developers, research scientists, field technicians, account executives, and more.
- **Perks**: Not explicitly listed, but emphasis on 24/7 remote support, always-on AI improvements, and no contract lock-in suggests a team that values reliability and customer success.
- **Engineering culture**: Focus on solving hardest problems in machine intelligence; teams work at the edge of AI, robotics, and operations.

## Sources
1. [tutorintelligence.com](https://tutorintelligence.com/)
2. [tutorintelligence.com/about](https://tutorintelligence.com/about)
3. [LinkedIn Company Page](https://www.linkedin.com/company/tutorintelligence)
4. [Built In Boston – Tutor Intelligence](https://www.builtinboston.com/company/tutor-intelligence)

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