Senior Computer Vision Engineer
Dexterity is an enterprise Physical AI and robotics company deploying AI-powered industrial robots for logistics and manufacturing operations.
Funding history
Investors
About Dexterity, Inc.
Dexterity develops a full-stack Physical AI platform, including its Foresight world model and Mech industrial robot, for production material-handling work such as trailer loading and unloading, palletizing, depalletizing, aircraft loading and unloading, and singulation.
Skills
Candidate Availability
Required and preferred rules are kept separate and reflect the wording in the original posting.
About the Role
You will write computer-vision code and build machine-learning models. You will scale ML pipelines, work with segmentation, RGBD datasets, and point clouds, and optimize low-latency inference. You will gather, augment, label, and curate data; train, deploy, troubleshoot, and improve production models.
Requirements
- BS or MS in Computer Science, Machine Learning, or a related discipline, or equivalent experience
- 5 or more years of related work experience
- Experience with OpenCV and Open3D
- Experience building, training, and deploying production ML models from scratch using PyTorch and TensorFlow
- Strong knowledge of Modern C++ and Python
- Experience using profilers and debuggers to optimize code
- Experience building and maintaining production code
- Experience with serving architectures such as NVIDIA Triton, TorchServe, TensorFlow Serving, and Flask
- Strong knowledge of machine learning and academic papers
- Experience with cloud infrastructure such as AWS, GCP, or Azure for training and serving pipelines
- Ability to solve problems across interconnected systems, pipelines, and applications
- Experience with Git and modern CI pipelines
Responsibilities
- Write computer vision code
- Build machine learning models for computer vision
- Update and scale ML pipelines
- Implement semantic and instance segmentation models
- Build highly performant models and serving architectures
- Gather, augment, label, and curate datasets
- Study model performance and optimize model quality
- Train, deploy, and troubleshoot models in the field
Hiring Process
Applications may be reviewed with AI tools; resumes and responses may be analyzed, and application materials assessed for inconsistencies or verification signals. The recruitment team uses these tools, while final hiring decisions are made by humans.