Senior Machine Learning Infrastructure Engineer

• Design and develop scalable, high-performance systems for training, inference, deploying, and monitoring ML models at scale
• Build and maintain efficient data pipelines, model versioning systems, and experiment tracking frameworks
• Collaborate with cross-functional teams, including ML researchers and engineers, to identify bottlenecks and improve platform usability
• Implement distributed systems and storage solutions optimized for machine learning workloadsDrive improvements in CI/CD workflows for ML models and infrastructure
• Ensure high availability and reliability of the ML platform by implementing robust monitoring, logging, and alerting systems
• Stay current with industry trends and integrate relevant tools and frameworks to enhance the platform
• Mentor junior engineers and contribute to a culture of technical excellence

Benefits

• Unlimited PTO: Unlimited paid time off on top of company holidays to recharge and enjoy as needed
• Flexible working: Flexibile work arrangements and technology to help manage life’s demands at the office and remote
• Health benefits: Tiered options in medical, dental, and vision insurance to fit your unique needs
• Future building: Equity, 401k and FSA options for all eligible employees to help build for your future
• Professional development: Company-sponsored professional development opportunities such as conferences, training, and workshops
• Catered lunches: Daily catered lunches at both of our Santa Clara and Fremont offices

• Ability to learn and adapt to new technologies and contribute in a productive environment
• Good oral and written communication skills
• Deploy and manage resources across multiple cloud platforms (AWS, GCP, or on-prem environments)
• Extensive software design and development skills
• MS in Computer Science, Electrical Engineering, or related field
• Deep understanding of containerization, orchestration technologies, distributed ML workload, and experiment tracking tools (e.g., Docker, Kubernetes, multiprocessing, Kubeflow, and mlflow)
• Proficiency in at least one deep learning framework, such as PyTorch and data pipeline tools (e.g., Apache Airflow, Prefect)
• Proficiency in in Python, C++, SQL
• 3+ years of software engineering experience with a focus on ML infrastructure or distributed systems
• Strong knowledge of distributed systems, databases, and storage solutions
• Familiarity with fundamental deep learning architectures, such as Convolutional Neural Networks (CNNs) and Transformer models
• Experience working with autonomous vehicles or robotics
• Experience in building large-scale ML datasets, MLOps pipelines, and distributed computing frameworks like Ray