Senior/Staff Machine Learning Engineer, Dexterity

• Experience with RGBD datasets and point clouds is very important to the type of problems you will encounter in this role and experience in the areas of ML and CV for autonomous vehicles or robotics is very useful
• In addition, a solid grounding in writing optimized code in c++ and python as well as understanding the limitations of languages and frameworks is critical
• Additionally, familiarity with cloud infrastructure to build training and serving pipelines is also a key aspect to this role
• At Dexterity, our use cases require low latency inference that approaches real time and therefore you must be able to build highly performant models and serving architectures
• While an understanding of open source solutions is a useful prerequisite, we also require that the candidate has experience building their own models and setting up training pipelines
• BS/MS/Ph.D. in Computer Science, Machine Learning or a related discipline, or equivalent experience
• 5 or more years of related work experience
• Prior 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 like NVIDIA Triton, TorchServe and Tensorflow Serving, flask and others
• Strong academic background and knowledge of ML and academic papers
• Experience with cloud infrastructure such as AWS, GCP, Azure etc. and experience using this infrastructure to create training and serving pipelines
• Ability to trace and solve problems across interconnected systems, pipelines and applications
• Experience deploying software and configuring distributed systems
• Experience with Git and modern CI pipelines

Desirable

• In this role at Dexterity, you will be working on building Machine Learning models for computer vision
• The role will involve understanding our current systems and their limitations and bringing your expertise in augmenting and developing ML models
• Additionally, you will be responsible for updating and scaling our current ML pipelines to cover more scenarios and improve accuracy
• You will leverage your expertise around the subfields of semantic segmentation and instance segmentation, keep up to date with the latest developments in these areas and implement them using platforms like Pytorch, Tensorflow etc.
• Your job will not only be finding and perfecting the right model(s) and architecture, but also identifying key problems throughout our system that can be solved with machine learning
• The end to end process of gathering, augmenting, labeling data and curating datasets, studying performance and optimizing quality of the model, as well as training, deploying and troubleshooting models on the field will be under your purview