Tech Lead Applied Scientist

About the role:

Samsara's AI has helped prevent accidents that would have harmed an estimated 380,000 people per year. Every model we ship runs in the real world, on real roads, protecting real drivers. 

This is the most senior individual contributor role on our Safety AI team. The role is focused on taking models from research to production in safety-critical systems.

You'll sit at the intersection of cutting-edge computer vision research and production systems that run across millions of IoT devices globally. The models you build run on the edge, in real time, on the trucks and vans and heavy equipment that keep the world moving.

• Scale: Millions of AI dash cams, 100B+ miles/year (~99% of U.S. roads), 25T+ data points
• Problems: Driver behavior understanding, 3D scene reasoning, and long-tail event detection in real-world environments
• Stack: Modern vision models (e.g. transformers), large-scale training pipelines, and optimization for edge inference on constrained devices

This is a remote position open to candidates residing in the US. 

You should apply if:

• You want to impact the industries that run our world: Your efforts will result in real-world impact – helping to keep the lights on, get food into grocery stores, reduce emissions, and most importantly, ensure workers return home safely.
• You are the architect of your own career: If you put in the work, this role won’t be your last at Samsara. We set up our employees for success and have built a culture that encourages rapid career development, countless opportunities to experiment and master your craft in a hyper growth environment.
• You’re energized by our opportunity: The vision we have to digitize large sectors of the global economy requires your full focus and best efforts to bring forth creative, ambitious ideas for our customers.
• You want to be with the best: At Samsara, we win together, celebrate together and support each other. You will be surrounded by a high-calibre team that will encourage you to do your best. 

In this role, you will: 

• You will be the technical thought leader and you will lead the design and implementation of our most critical Safety AI initiatives including real-time perception systems that run across millions of edge devices in the physical world and the cloud. The scale and constraints make this an extremely unique applied ML problem. You will:
  • Architect end-to-end computer vision pipelines for real-world safety detection — object detection, tracking, semantic segmentation, multi-camera fusion, and beyond
  • Drive the technical roadmap for edge and cloud perception, including how we optimize and deploy models on constrained hardware without sacrificing accuracy
  • Partner closely with our hardware and firmware teams — we build our own devices, which means you'll have a rare ability to co-design the full stack
  • Work with petabyte-scale multimodal data (video, sensor, telematics, diagnostics) to train and iterate on production models
  • Stay at the frontier of CV and perception research and translate what matters into shipped product
  • Mentor and technically guide senior scientists and engineers across the team
  • Bring clarity to ambiguous problems — translating customer and business needs into precise, solvable engineering challenges.
• Champion, role model, and embed Samsara’s cultural principles (Focus on Customer Success, Build for the Long Term, Adopt a Growth Mindset, Be Inclusive, Win as a Team) as we scale globally and across new offices

Minimum requirements for the role:

• Master’s or PhD in Computer Science, Electrical Engineering, Robotics, Computer Vision, or a related quantitative field.
• 10+ years as a scientist or ML engineer, with experience leading end-to-end AI systems in production.
• Deep expertise in computer vision (e.g., object detection, tracking, segmentation) for real-world environments.
• Experience with multimodal perception and sensor fusion (e.g., camera, lidar, radar, GPS/IMU).
• Experience with transformer-based architectures and vision-language models (VLMs/VLAs).
• Experience building and deploying real-time or edge ML systems optimized for low-latency inference.
• Demonstrated ability to drive systems from research through production deployment, including performance optimization and reliability at scale.

An ideal candidate also has:

• Experience applying perception to real-world systems such as driver assistance, fleet safety, or operations automation.
• Publications or patents at top-tier venues (CVPR, ICCV, ICRA, NeurIPS).