Senior Machine Learning Engineer - Object Detection + Semantic Segmentation

Gravis Robotics is a startup that turns heavy construction machinery into intelligent and autonomous robots. Our unique combination of learning-based automation and augmented remote control lets one operator safely conduct a fleet of earthmoving machines in a gamified environment. Our team has decades of combined academic and industrial experience operating at the cutting edge of large-scale robotics, and is rapidly growing to bring that expertise into a trillion dollar industry through active deployments with market leaders.
What we build At Gravis, we engineer solutions at the nexus of hardware and software every day: bringing new perception and control technologies onto heavy machinery. We turn ordinary excavators into autonomous robots! Our Rooftop Autonomous Control Kit (RACK) combines sensors, compute, communication and networking modules toward a manufacturer-agnostic solution that can be retro-fitted to a variety of construction machines regardless of type and age. 
Who you will work withWe are the Perception Team at Gravis! A small group of engineers committed to building systems that help sense and contextually understand the environment - enabling accuracy, productivity and safety of operation. We specialize in sensor software integration, high accuracy state estimation, environment mapping, and scene understanding. You will also collaborate with other teams on topics such as motion planning, user interface and automated deployment.
About the roleWe are seeking a highly skilled and experienced Senior ML Engineer to spearhead the development and deployment of state-of-the-art object detection and scene segmentation systems — while ensuring production quality implementation and timely execution. You have a chance to shape the development of a new industry: automated heavy construction!

Your Responsibilities

• Lead the development and deployment of advanced real-time perception and deep learning models for autonomous heavy machines, focusing on multi-modal object detection / tracking and semantic segmentation.
• Establish performance metrics and optimize testing procedures to ensure deployment of a robust, production-grade system.
• Collaborate closely with multi-disciplinary experts across teams and mentor junior engineers/interns.
• Partake in hiring initiatives to help grow the team.
• Occasionally visit test sites for live operations/demos.

Required Qualifications

• Master’s or PhD in Computer Science, Mechanical Engineering, Electrical Engineering or a related field.
• Experience with setting up and maintaining machine learning pipelines, from data collection to model deployment
• Experience in developing and implementing robust perception / computer vision algorithms.
• Experience in writing production-quality C++/Python code in a Linux development environment.
• Extensive experience with deep learning frameworks (pytorch, tensorflow, etc) using image, LiDAR, and/or radar data.
• Proficiency with  common robotics & perception frameworks ( e.g. OpenCV, PCL, Open3D, ROS 2, Nvidia HW/SW ecosystem, etc.)
• Excellent project management skills with the ability to prioritize tasks, manage resources, and meet deadlines.
• Excellent communication skills with the ability to effectively convey technical concepts to both technical and non-technical stakeholders.

Additional Beneficial Skills

• Experience with Nvidia based Hardware-Software ecosystems
• Experience with Rust programming

Don't meet every requirement? If you're enthusiastic about the role but your experience doesn't match every qualification, we still encourage you to apply. You may still be the perfect fit for another position - tell us about how you could accomplish your best at Gravis with a cover letter.
This is an opportunity to join a dynamic and versatile team, and to be part of a young startup that will revolutionize heavy construction. Gravis Robotics offers a fair market salary and a working location in the vibrant city of Zurich. As a forward-facing startup, we understand that work-life balance and flexibility are important considerations for many professionals: If you are a highly qualified candidate with the requisite skills and experience, we encourage you to apply and discuss your preferred working arrangement during the interview process.
Gravis is an equal opportunity employer. We are committed to building an inclusive and diverse team, and do not discriminate based upon race, color, ancestry, national origin, religion, sex, sexual orientation, age, gender identity, gender expression, disability, veteran status, or other legally protected characteristics.
We are an international team that is working to solve problems with a global impact: to facilitate efficient communication and collaboration, proficiency in English is a requirement for all roles.