About Alloy Robotics
We believe robotics is the industry of our lifetime, and data is its lifeblood.
Alloy is the AI-native data platform for robotics teams. Robot fleets generate enormous volumes of logs, video, telemetry, and mission data, and most of it ends up in silos where nobody can use it. Alloy turns it into one queryable layer with AI agents on top: engineers ask questions in natural language, get automated mission reports, and catch failures before they compound. The point is simple. Your robot breaks, you find out why, and your best people spend their time building the next robot instead of digging through the last one.
We've raised $16m, backed by Square Peg, Blackbird, and Airtree, with angels including senior leaders from OpenAI, Anthropic, Tesla, Waymo, and Halter. We partner with robotics teams across defence, agriculture, maritime, humanoids, construction, and medical, including Advanced Navigation, DroneForge, Breaker Industries, and Puralink. We're a lean team building from Sydney, and are adding our first US hires.
Where we are, and where we're heading
The product works: fleets stream data in, engineers replay missions in 3D, agents answer and report. What turns a good evaluation into a customer who can't run their fleet without us is an engineer in the room. In the US, that's you.
About the role
You'll be the technical face of Alloy in the field: embedded with US robotics teams, getting their data flowing, building the analyses that prove value, and carrying what you learn back into the product. Half solutions engineering, half product engineering, all customer.
Travel is real: expect 50% or more, embed weeks flexing with pilots.
What you'll do
Own technical success end to end: onboarding, ingestion, integration
Run pilots to conviction: define success criteria, build to them
Embed on-site; be the engineer customers trust
Ship field learnings back into the platform
Build playbooks that make each deployment faster than the last
You may be a good fit if you have
3+ years of engineering with real customer-facing time
Strong Python and data skills in a modern web stack
Presence in a technical room: workshops, live debugging, off-script demos
Self-direction in the field, 50%+ travel appetite
Based in or near San Francisco
Strong candidates may also have
Robotics or hard-tech exposure (ROS/MCAP a plus)
LLM and agent-systems experience
Zero-to-one startup time
How we work
We're building the company we always wanted to join: small, fast, and serious about the craft. The bar is work you'll be proud of in five years, shipped this week. Everyone owns outcomes end to end: the interesting problems don't come with instructions, and nobody will hand you a playbook. We question requirements, delete what shouldn't exist, and spend our energy where customers feel it. If you want clear lanes and a manager to set your week, we'll frustrate you. If you want real problems, real pace, and the best work of your career, you'll fit right in.
Compensation
Competitive salary
Additional compensation and benefits may include equity and flexible working arrangements
If this reads like you but you don't tick every box, apply anyway. We care more about trajectory than checklists.
Alloy Robotics is an equal opportunity employer. We consider all qualified applicants without regard to race, color, religion, sex, sexual orientation, gender identity, national origin, veteran, or disability status.
Skills Required
- 3+ years of engineering with real customer-facing time
- Strong Python and data skills in a modern web stack
- Presence in technical rooms for workshops, live debugging, and off-script demos
- Self-direction in the field and willingness to travel 50%+
- Based in or near San Francisco
- Robotics or hard-tech exposure (ROS/MCAP)
- Experience with LLMs and agent systems
- Zero-to-one startup experience
What We Do
Alloy provides a robotics data platform that unifies images, time-series telemetry and logs so engineers can search and analyze robot missions in plain English. The product creates instant mission reports, auto-clusters similar failures across fleets, and surfaces patterns for faster debugging and training data generation—accelerating development and reducing time spent hunting for relevant robot data.
