Responsibilities

• Train, deploy, and maintain various ML models on cloud and on-premise infrastructure
• Develop pipelines and tools for all components of the ML lifecycle – from training, evaluation, and optimization to deployment
• Partner closely with research and engineering teams on model architecture design, implementation, experimentation, and productionalization
• Promote quality and reliability through regular code reviews
• Contribute to the vibrant research and learning environment of the Institute by staying up to date on the latest innovations in ML architectures, frameworks and applications to robotics 

Requirements

• BS or MS in computer science, engineering, or equivalent technical experience
• 6+ years overall experience (3+ post-Masters or PhD) as a machine learning engineer, software engineer, or applied scientist. 
• Experience writing production code for data processing, machine learning training, and/or serving in Python, C++, or similar 
• Experience with git, issue tracking, CI/CD, and modern software engineering practices
• Experience with cloud ecosystems such as GCP and AWS
• Experience with deep learning libraries and frameworks such as PyTorch, TensorFlow and Flax
• Hands-on experience implementing and training deep learning models Experience with state-of-the-art deep-learning techniques such as transformers, diffusion models, and multi-modal modeling, in domains such as robotics, computer vision, and NLP.

Bonus - Nice to Have

• Hands-on experience with one or more of reinforcement learning, imitation learning, incremental learning, inference optimization and model compression
• Experience with robotics simulation platforms such as MuJoCo, Isaac Sim, and Drake
• Experience deploying models to devices such as robotic embodiments, and/or experience with ROS
• Experience with parallelized data processing frameworks such as Hadoop, Spark, and Ray
• Experience scaling training to multi-gpu and multi-node environments with Ray, Pytorch Lightning, Kubeflow, or similar
• Experience with MLOps (model versioning, model and data lineage, monitoring, model hosting and deployment, scalability, orchestration, continuous learning)
• Experience with Docker, Kubernetes, cloud computing, or similar applications 
• Experience with orchestration workflows with tools such as Airflow, Kubeflow, or AWS Step Functions
• DevOps experience (e.g. CI/CD Pipelines, Infrastructure as Code, containers)