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Gaming, Blockchain, Web3, Software, Digital Distribution

Research Scientist

New York, NYOnsiteFull Time$200,000–$350,000 /yrPosted 2 months agoHidden Gem · YC Startup

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Required skills

  • AI robots
  • industrial AI
  • warehouse automation
  • e-commerce order packaging
  • robot foundation models
  • fine-tuning large models
  • IL
  • RL
  • model robustness
  • model performance

Nice to have

  • ML to robotics
  • Machine Learning
  • Robotics

Role summary

Ultra is seeking high-impact researchers and engineers to bridge the gap between robot foundation models and production deployment for industrial AI robots. The role focuses on improving robot speed, reliability, and learning capabilities through post-training and new research. Responsibilities include leading research efforts, integrating models into the production robotics stack, and scaling models across customer sites. The ideal candidate has experience fine-tuning large models, operates with high agency, and cares about evaluation and benchmarking. Experience applying ML to robotics is a plus.

# About Ultra

Ultra builds industrial AI robots that are working in the real world today. Our robots automate mission-critical warehouse labor end-to-end, starting with e-commerce order packaging. We deploy fast, learn from real production data, and iterate aggressively. We already have a quickly growing number of revenue-generating robots operating in U.S. warehouses, and are now scaling towards thousands of deployments over the next few years. Our mission is simple: build the world's most useful and deployable robot.

**WARNING:** Robotics is not for the faint of heart. There are no silver bullets here. It is tiresome, unglamorous, and at times brutal work. The only way out is through, and the only way through is treacherous and unknown. **Success will require teamwork** - so we are building a world-class team to tackle some of the most exciting problems out there. We hope you join us.

# **About the role**

We are looking for high-impact researchers and engineers to help bridge the gap between robot foundation models and production robot deployment. Speed and reliability are the two metrics that matter the most for industrial customers, yet most robot models struggle on both. You will post-train robot foundation models and do new research to hill climb task performance in our research lab and at real customer deployments. 

# **What You’ll Do**

- Improve our robots’ ability to quickly learn new tasks and to execute those tasks quickly and reliably
- Lead research efforts around improving our models’ robustness and performance for real world tasks
- Integrate trained models with our production robotics stack and work with deployment teams to scale models across real customer sites

# **Who You Are**

- Have experience with fine-tuning large models for downstream tasks (IL, RL, etc.)
- Operate with high agency and are able to own significant research efforts end to end
- Care deeply about evaluation, benchmarking, and failure analysis
- A generalist who enjoys getting your hands dirty across the whole stack ML stack 
- Passionate about seeing robots make an impact in the real world

# **Bonus Points**

- Experience applying ML to robotics, with hands-on robot experience
- MS/PhD in Computer Science, Machine Learning, Robotics, or a related field

**Expected Compensation**

$200,000 - $350,000/annual salary + equity + benefits

This compensation range may be inclusive of several career levels at Ultra and will be narrowed during the interview process based on the candidate’s experience and qualifications. Adjustments outside of this range may be considered for candidates whose qualifications significantly differ from those outlined in the job description.

**Equal Employment Opportunity Statement**

We are an Equal Opportunity Employer. We do not discriminate based on race, religion, color, national origin, gender, sexual orientation, age, marital status, veteran status, or disability status. We are committed to creating a diverse and inclusive environment and encourage applicants from all backgrounds to apply.
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