Machine Learning Engineer

Machine Learning Engineer

Location: Los Altos, CA (Hybrid)

Term: 3-month contract (potential to extend/go FTE)

Job Content:

• 4+ years of professional ML engineering experience at an AI/ML-focused organization.

• Familiarity with the state-of-the-art in behavior learning, language, and/or computer vision.

• Experience training large-scale foundation models (VLMs, text-to-video models, etc) utilizing distributed training and high-performance optimization techniques such as quantization, mixed precision, model parallelism, data parallelism or FSDP.

• Extensive practical experience with PyTorch.

• Strong proficiency in Python and software development best practices such as unit testing, documentation, code review, continuous integration, and dependency management.

• Familiarity with data pipelines, model serving and optimization, cloud training, and dataset management.

• An ability to move fast and switch between modes of rapid prototyping and robust implementation as required.

Nice to have:

• Experience deploying models on embodied systems/robots.

• Experience working in mixed teams of research scientists and engineers.

• Experience Amazon EC2, S3, and/or Sagemaker.

• Experience with Bazel.

Key Qualifications & Requirements

• Automotive Domain Expertise Proven track record in AI/ML applications within the automotive sector (highest priority).
• PyTorch Mastery Hands-on experience in building, training, and deploying models using PyTorch.
• AI/ML Engineering Experience 4+ years in professional machine learning engineering, ideally within AI/ML-focused organizations.
• Cutting-Edge Research Familiarity Knowledge of the latest advancements in behavior learning, language modeling, and/or computer vision.
• Large-Scale Model Training Expertise in training foundation models (VLMs, text-to-video, etc.) with distributed training and high-performance optimization techniques (quantization, mixed precision, model/data parallelism, FSDP).
• Software Engineering Excellence Strong proficiency in Python with best practices including unit testing, documentation, code review, CI/CD, and dependency management.
• End-to-End AI Workflow Knowledge Experience in data pipelines, model serving & optimization, cloud-based training, and dataset management.
• Agility & Adaptability Ability to rapidly prototype and transition to production-grade implementations as project needs evolve.