Who we are:
Founded in 2017, Gatik is the leader in autonomous middle mile logistics. We deliver goods safely and efficiently using medium duty trucks with a focus on short-haul, B2B logistics for Fortune 500 customers such as Walmart and Loblaw. Gatik enables our customers to optimize their hub-and-spoke operations, enhance service levels and product flow across multiple locations while reducing labor costs and meeting an unprecedented expectation for faster deliveries.
About the role:
We're currently looking for research engineers with specialized skills in LiDAR, camera, and radar perception technologies to enhance our autonomous driving systems' ability to understand and interact with complex environments. In this pivotal role, you'll be instrumental in designing and refining the ML algorithms that enable our trucks to safely navigate and operate in complex, dynamic environments. You will collaborate with a team of experts in AI, robotics, and software engineering to push the boundaries of what's possible in autonomous trucking.
What you'll do:
- Design and implement cutting-edge perception algorithms for autonomous vehicles, focusing on areas such as sensor fusion, 3D object detection, segmentation, and tracking in complex dynamic environments
- Design and implement ML models for real-time perception tasks, leveraging deep neural networks to enhance the perception capabilities of self-driving trucks
- Lead initiatives to collect, augment, and utilize large-scale datasets for training and validating perception models under various driving conditions
- Develop robust testing and validation frameworks to ensure the reliability and safety of the perception systems across diverse scenarios and edge cases
- Conduct field tests and simulations to validate and refine perception algorithms, ensuring robust performance in real-world trucking routes and conditions
- Work closely with the data engineering team to build and maintain large-scale datasets for training and evaluating perception models, including the development of data augmentation techniques
What we're looking for:
- Master’s or PhD in Electrical Engineering, Computer Science, Robotics, or a related field, with a focus on sensor fusion, signal processing, or machine learning
- At least 5 years of hands-on experience in developing perception systems for autonomous vehicles, with a strong portfolio in LiDAR and camera-based perception, modern perception technologies, and synthetic data application
- Expertise in programming languages and tools critical for high-performance computing and machine learning, including C++, Python, CUDA, TensorFlow, and PyTorch
- Hands-on experience with sensor calibration, vehicle dynamics, and environmental modeling
- Strong foundation in data structures, algorithm design, and complexity analysis specifically tailored to real-time, high-reliability systems
- Experience with deep learning optimization for edge deployment, including quantization, pruning, and knowledge distillation
- Demonstrated ability to publish research findings in top-tier technical journals and conferences
More about Gatik:
With headquarters in Mountain View, CA and offices in Canada, Texas, Louisiana and Arkansas, Gatik is establishing new standards of success for the autonomous trucking industry every day. Visit us at
Gatik for more company information and
Jobs @ Gatik for more open roles.
Gatik News:
Taking care of our team:
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. Our culture emphasizes the importance of collaboration, respect and agility.
We at Gatik strive to create a diverse and inclusive environment where everyone feels they have opportunities to succeed and grow because we know that together we can do great things. We are committed to an inclusive and diverse team. We do not discriminate based on race, color, ethnicity, ancestry, national origin, religion, sex, gender, gender identity, gender expression, sexual orientation, age, disability, veteran status, genetic information, marital status or any legally protected status.
