Build the Path Forward

At Path Robotics, we’re building the future of embodied intelligence. Our AI-driven systems enable robots to adapt, learn, and perform in the real world closing the skilled labor gap and transforming industries. We go beyond traditional methods, combining perception, reasoning, and control to deliver field-ready AI that is risk-aware, reliable, and continuously improving through real-world use.

Big, hard problems are our everyday work, and our team of intelligent, humble, and driven people make the impossible possible together.

You will play a mission-critical role in architecting our data platform, enabling our machine learning and robotics teams to move faster, experiment more effectively, and deploy AI solutions into production.

This is an opportunity to push our data infrastructure forward and drive value across the business. Reporting to the Data Engineering Manager, you will collaborate with a talented team of data engineers, machine learning engineers and software engineers to deliver clean, reliable, and scalable data products that fuel experimentation, production models, and cutting-edge AI development.

What You’ll Do

• Partner with software, ML, robotics, and analytics teams to understand their data needs and turn them into well-modeled tables and data products.
• Develop and extend data models and pipelines assets that support experimentation, model training and validation, reporting, and business decisions.
• Model large-scale, multi-modal data such as sensor streams, application telemetry, system logs, and unstructured blob data. Identify opportunities for this data to support our mission. 
• Monitor pipeline health and data quality, troubleshoot failures, and improve tests and alerting in order to build trust in our data assets.
• Apply and strengthen the team's standards for governance, lineage, documentation, and data contracts.
• Write clean, tested, well-documented Python and SQL, and take part in team code reviews.
• Improve the data platform's usability, performance, and tooling, and evangelize enhancements among our user base.

Who You Are

• 2+ years of experience in data engineering or a closely related role, building and supporting production data pipelines.
• Strong proficiency in SQL and Python.
• Hands-on experience with tools like AWS, Snowflake, Dagster, dbt, or an equivalent modern data stack.
• Solid grasp of data modeling, warehousing concepts, and pipeline orchestration.
• Solid grasp of data modeling, warehousing concepts, and pipeline orchestration.
• Curious about the problems your data solves. You enjoy working directly with stakeholders to deliver data that drives real decisions.
• You take ownership of your work, communicate clearly, and want to learn from and contribute to an experienced team.
• Bonus: experience working with sensor data, robotics logs, manipulation data, or other high-volume, real-world datasets.

Why You’ll Love Working Here

• Daily free lunch to keep you fueled and connected with the team
• Flexible PTO so you can take the time you need, when you need it
• Comprehensive medical, dental, and vision coverage
• 6 weeks fully paid parental leave, plus an additional 6–8 weeks for birthing parents (12–14 weeks total)
• 401(k) retirement plan through Empower
• Generous employee referral bonuses—help us grow our team!

Who We Are

Regulatory and Compliance Requirements
This position may support work performed under U.S. Government contracts and requires compliance with applicable federal regulations, including FAR and DFARS where applicable. The role may involve access to export-controlled information subject to ITAR and/or EAR. Candidates must be able to comply with U.S. export control regulations, which may include meeting the definition of a “U.S. Person” where required. This position may also involve handling Controlled Unclassified Information (CUI) and requires adherence to company and federal data protection and confidentiality standards. Employment may be contingent upon successful completion of a background check consistent with applicable requirements.