Senior Software Engineer - Robotics, Machine Learning & Picking IntelligenceAbout the RoleWe are looking for a senior robotics engineer with strong applied machine learning experience to help own and evolve the intelligence behind robotic picking in production.
This is not a research role. You will build, deploy, and continuously improve perception, decision-making, and learning systems that directly affect robot performance in customer warehouses.
You'll work across robotics, ML, and system architecture to improve:
- Pick success and reliability
- Segmentation and perception quality
- Grasp and suction selection strategies
- Placement and exception handling
- Overall system robustness in the real world
What You'll Work OnDepending on your strengths and interests, you will contribute across areas such as:
- Improve pick success, reliability, and throughput across production robots
- Build and deploy perception and segmentation models for warehouse environments
- Develop intelligent picking, retry, and placement strategies that reduce exceptions
- Integrate ML outputs into real-time robotic decision systems
- Work hands-on with robots, cameras, and sensor data to harden systems
- Use production data to continuously improve performance and robustness
- Collaborate closely with hardware, operations, product, and customer teams
Who We're Looking ForWe're looking for a senior-level engineer who is either strong across robotics and ML - or very strong in one with the ability and curiosity to grow in the other.
Required Experience- 5+ years of professional experience in robotics, ML engineering, or related fields
- Strong Python experience
- Experience delivering production-level code
- Hands-on experience with real-world systems, including robotics, sensors, or deployed ML
- Strong problem-solving mindset and comfort working across system boundaries
Preferred Qualifications- Applied machine learning / MLOps experience (model deployment, iteration, monitoring)
- Computer vision experience (segmentation, detection, depth, perception pipelines)
- Strong kinematics background
- Experience with integrating and calibrating color and depth sensors
- Experience with manipulation, picking, grasping, or physical automation
- Familiarity with Docker, Linux, and production infrastructure
- Experience using production data to draw actionable insights
- DevOps experience
