- Employment
- Internship
- Seniority
- Junior
About the role
About the team
Droyd builds autonomous robotic systems that automate repetitive manual work in real environments. Our robots operate under tight compute, latency, and reliability constraints, so learning systems must work cleanly on real hardware.
Our AI team builds the models and inference systems that let robotic arms see, reason, and act. This work runs on deployed robots, not demos.
About the role
As a Machine Learning Intern at Droyd, you’ll work directly on the learning and inference systems that power our robotic arms. You’ll train models, run experiments, and help push research into production.
You’ll work closely with AI researchers, software engineers, and hardware teams, and contribute to systems that ship to real robots.
This role is based in San Francisco, CA. We’re an in-person company. We build faster that way.
In this role, you’ll
Work across the ML stack, from training to inference
Train and evaluate models that run on low-payload robotic systems
Run experiments, analyze results, and document findings
Learn how model design, data quality, and hardware constraints affect real-world performance
Support deployment and testing of models on robotic hardware
We’re looking for candidates who
Are current juniors or seniors (or equivalent) studying computer science, machine learning, AI, or a related field
Have coursework or hands-on experience training ML models using frameworks like PyTorch or JAX
Are willing to balance school and work in a fast-moving environment
Are curious about robotics and interested in how learning systems behave in the real world
Take ownership, ask good questions, and can carry projects forward with guidance
About Droyd
Droyd builds autonomous robotic systems to automate manual work for enterprises. We design the hardware, collect our own data, and train models that operate under real-world constraints.
If we do this right, robots become dependable tools people rely on every day.
Join us and help build systems that ship.
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