Machine Learning Engineer Intern (MS/PhD, 2027 Summer)

Waymo LLC; San Francisco (CA); Hybrid; USD 55,000 - 83,000; Full time

Benefits offered by this job

Medical, dental, and vision insurance

Mental health programs

Onsite wellness centers

Educational reimbursement

Paid time off

Job summary

Waymo LLC invites PhD/MS students to join our internship focused on training and evaluating deep learning models for autonomous driving. You will work with JAX/Flax or PyTorch, build data pipelines, and refine losses while iterating on fine-tuning strategies.

You will collaborate with UEM Core, Data Science, and release-eval stakeholders, delivering design docs and a final intern presentation. You will land production-quality code in a shared codebase, demonstrate rigorous evaluation, and

Qualifications

• Hands-on experience training and evaluating deep learning models, building data pipelines, selecting losses, debugging training runs.
• Strong programming skills in C++/Python with ML fundamentals and rigorous experiment design.
• Experience with transformer and sequence models, multi-task learning, transfer learning, or domain adaptation.

Responsibilities

• Train and fine-tune a large multi-task transformer over driving-log sequences.
• Design and run offline and end-to-end evaluations using PR-AUC, calibration quality, and metric sensitivity.
• Land production-quality code in a shared codebase and communicate results via design docs and presentations.

Skills

C++/Python

ML fundamentals

Experiment design

JAX/Flax experience

TPU/GPU distributed training

Data pipelines

Autonomous driving familiarity

Academic publishing

Education

PhD or MS in CS/ML

Tools

JAX/Flax

PyTorch

TensorFlow

Job description

• Train and fine-tune a large multi-task transformer over driving-log sequences, in JAX/Flax on TPUs - iterating on fine-tuning strategies, training data mixtures, and losses to improve evaluation quality for the hillclimbing workflow
• Design and run rigorous offline and end-to-end evaluations - PR-AUC, calibration quality, and metric sensitivity on real hillclimbing A/B runs - and build the dataset and evaluation pipelines needed to produce them
• Land production-quality code in a shared, high-traffic codebase, and communicate results through a design doc, team deep dives, and a final intern presentation, partnering with UEM Core, Data Science, and release-eval stakeholders

Benefits

• Medical, dental, and vision insurance for employees and dependents
• Employee assistance programs focused on mental health
• Personalized workplace adjustments for diverse needs and abilities, including physical, mental, and neurodivergent considerations
• Access to mental health apps
• Onsite wellness centers
• Medical advocacy program for transgender employees
• Second medical opinion for you and your loved ones
• Counseling services
• Support programs including menopause benefit
• Competitive compensation
• Regular bonus and equity performance grant opportunities
• Generous 401(k) and regional retirement plans
• 1-on-1 financial coaching
• Annual cross-company compensation review and pay equity analysis
• Fertility and growing family assistance
• Parental leave and baby bonding leave
• Backup childcare
• Elder care and support
• Survivor income benefit
• Caregiver leave
• Paid time off, including vacation, bereavement, sick leave, parental leave, disability, and holidays
• Jury duty leave
• Military leave
• Hybrid work model with remote work opportunities also available
• Educational reimbursement
• Peer learning and coaching platform
• Donation matching programs
• Employee resource groups
• Volunteer hours
• Internal community groups and local culture clubs
• Inspiring spaces to work, recharge, and collaborate with fellow Waymonauts
• On-site meals and snacks
• Fitness centers, massage programs, and ergonomic support
• On-demand fitness, wellbeing, and cooking classes
• Commuter benefits

Hands-on experience training and evaluating deep learning models in a modern framework (JAX, PyTorch or TensorFlow), including building data pipelines, choosing losses, and debugging training runsStrong programming skills in C++/Python, plus a solid grounding in ML fundamentals: precision/recall trade-offs, class imbalance, evaluation metric selection, and rigorous experiment designCurrently enrolled in an PhD or MS program in Computer Science, Machine Learning or a related field, returning to the program after the internshipAuthorship of published papers in top-tier AI/ML, data mining, or computer vision conferences (e.g., NeurIPS, ICML, ICLR, KDD, CVPR, CoRL, SIGMOD, VLDB, ACL)Research or applied experience with transformer and sequence models, multi-task learning, transfer learning or domain adaptation, and parameter-efficient fine-tuning of large pretrained modelsExperience with JAX/Flax, distributed training on TPUs or GPUs, and large-scale data processing (MapReduce-style pipelines, SQL) for building training and evaluation datasetsFamiliarity with autonomous driving, robotics, or simulation; and/or with probability calibration, uncertainty quantification, importance sampling, active learning, or rare-event and imbalanced-data modeling