Machine Learning Engineer

What you will do:

• Model Deployment & Maintenance: Train, deploy, and sustain a variety of ML models on both cloud and on-prem infrastructure to enhance robotics capabilities
• Pipeline Innovation: Build and refine ML pipelines, encompassing every lifecycle phase from training and evaluation to optimization and deployment
• Collaborative Development: Partner closely with research and engineering teams to design, test, and implement robust model architectures suited for production
• Quality & Reliability: Elevate code quality through regular peer reviews and champion best practices in our software processes
• Continuous Learning: Engage actively in our Institute’s vibrant research environment to stay at the cutting edge of advancements in ML architectures, frameworks and applications.

What you will bring:

• Technical Background: BS or MS in Computer Science, Engineering, or equivalent; 6+ years of experience (3+ with MS or PhD) as a machine learning engineer, software engineer, or applied scientist
• Expert Coding Skills: Proficiency in production-level data processing and ML training in Python, C++, or similar languages
• Engineering Best Practices: Proficient in software practices like version control (Git), CI/CD, and issue tracking
• Cloud Expertise: Hands-on experience with cloud platforms like GCP and AWS
• Machine Learning Frameworks Knowledge: Proficiency with deep learning frameworks such as PyTorch, TensorFlow, or Flax.
• Modeling Mastery: In-depth hands on experience with state-of-the-art ML techniques—transformers, diffusion models, multimodal modeling—applied across domains like robotics, computer vision, and NLP.

Extra Skills We Value:

• Advanced ML Techniques: Hands-on with reinforcement learning, imitation learning, incremental learning, or model optimization/compression.
• Simulation Experience: Familiarity with robotics simulators like MuJoCo, Isaac Sim, or Drake.
• Edge Deployment: Experience deploying models on robotic devices and/or with ROS.
• Big Data Processing: Skills in parallelized data frameworks like Hadoop, Spark, or Ray.
• Scalable Training Expertise: Familiarity with distributed training using tools like Ray, PyTorch Lightning, or KubeFlow.
• MLOps & Deployment: Expertise in MLOps practices (model versioning, monitoring, scalable deployment).
• Containerization & Orchestration: Experience with Docker, Kubernetes, and orchestration tools (Airflow, AWS Step Functions).
• DevOps & Automation: Proficiency in CI/CD pipelines, IaC, and containerized environments.