Gatik is the leader in autonomous middle mile logistics. We deliver goods safely, quickly, and efficiently in our fleet of medium-duty trucks, connecting people and communities to the goods they really need. With every delivery, we’re making the supply chain more reliable, keeping costs low, driving sustainability, and making the roads safer for all road users.
At Gatik, we believe in establishing new standards of success for the autonomous trucking industry every day. In 2021, we launched the world’s first fully driverless commercial delivery service with Walmart, a historic milestone that’s changing the way we view goods movement forever.
Gatik is a place to learn, lead and grow alongside industry veterans who are defining the future of logistics, and we want you to help us shape the next chapter in our journey.
We are seeking a senior or staff research engineer for our perception and prediction team to develop online lidar and imagery detection classification algorithms for static and dynamic objects in and around the roadway. Our perception and prediction team contributes to our multi-modal sensor capabilities, dynamic range extension, and fused perception and prediction. Ultimately, you will improve our object identification, classification, and tracking for better behavioral and intent prediction.
Through effective collaboration with our engineering disciplines – and your thought leadership on perception and prediction – you will contribute to our efforts to build the improved perception and prediction capabilities from design all the way through to production. We are seeking top minds to lead the development of perception capabilities for our autonomous vehicles. The ideal candidate will be a software expert who has overseen a process from the R&D phase through product shipment and has a passion for leading teams and developing real-world solutions.
If you enjoy developing object detection algorithms and enjoy working closely with talented and collaborative teammates, we’d love to talk to you!
Research and develop algorithms to identify, classify and track static and dynamic objects of interest in and around the roadway.
Integrate vision sensors output – including cameras and LIDAR – as model inputs in building algorithms
Create predictive models to track objects of interest over time and feed data to our decision-making models to guide immediate-term behaviors.
Collaborate with the mapping engineering team to ensure accurate, relevant map input including anomaly detection such as construction sites or missing traffic signals.
Build better models to estimate more information about an object including classification of the object, speed, and direction.
Partner with prediction engineering teams to anticipate the behavior of objects.
Masters’ or Ph.D. degree in CS, Robotics or related field
7+ years of related experience in machine learning for perception and prediction
Skilled software developer with fluency in C/C++ and/or Python
Experience in state-of-the-art deep learning, machine learning techniques
Experience in computer vision, tracking, sensor fusion
Experience working with multiple sensor modalities (lidar, imagery)
Strong background in optimization, linear algebra, statistics
Experience working with maps
Publications in a peer reviewed journal or conference (e.g. CVPR)
Working at Gatik
At Gatik, we connect people of extraordinary talent and experience to an opportunity to create a more resilient supply chain and contribute to our environment’s sustainability. We are diverse in our backgrounds and perspectives yet united by a bold vision and shared commitment to our values.
From our offices in the Bay Area, Toronto, and Texas, we pursue big goals with a relentless focus. We are builders who focus on delivering for our customers and each other. We recognize that the path toward excellence balances thoughtful debate with aligned action.
Individuals seeking employment at Gatik are considered without regard to race, color, religion, national origin, age, sex, gender, gender identity, gender expression, sexual orientation, marital status, pregnancy status, parent or caregiver status, ancestry, political affiliation, veteran and/or military status, physical or mental disability, or any other status protected by federal or state law.