Senior Software Engineer, ML Evaluation & Introspection Infrastructure (AI)

Location: Mountain View, California, United States; remote is listed, while employees are expected to work from the office at least 3 days per week.

Salary: $162,000–$260,000 per year for roles based in Mountain View, CA. Annual bonus, equity compensation, and benefits are also offered.

Company

Builds self-driving technology for safer and more efficient mobility and logistics, including autonomous trucks operating on public highways.

What you will do

• Build and scale evaluation pipelines comparing learned-model versions on large driving datasets, including offline evaluations, slice-based metrics, regression detection, and model-versus-model reports.
• Own embedding-generation and vector-search pipelines over versioned driving data, enabling engineers to search, cluster, and compare model representations.
• Build model-introspection infrastructure using attention, attribution, and ablation techniques to explain model decisions.
• Help apply LLMs, VLMs, and agents to triage, label, cluster, summarize, and curate driving scenarios and evaluation results.
• Work with ML engineers to define trustworthy, observable, and cost-efficient measurements for candidate models.
• Build the web app and CLI used by engineers and AI agents to find datasets, training runs, evaluations, and their costs.

Requirements

• Typically 5+ years of professional software engineering experience building backend, data, or ML infrastructure.
• Strong Python and proficiency with SQL.
• Experience designing and operating large-scale batch or data pipelines on cloud infrastructure, including backfills, retries, and cost management.
• Experience building systems that measure or evaluate something, such as metrics, experimentation, evaluation, or analytics platforms.
• Experience with distributed compute and container orchestration, such as Kubernetes, AWS Batch, or similar technologies.

Nice to have

• Experience with ML evaluation infrastructure, including evaluation harnesses, golden datasets, regression detection, and A/B model comparison.
• Experience with embeddings and vector search, or model-introspection and interpretability tooling.
• Experience using LLMs, VLMs, or agentic workflows for analysis, labeling, or data curation.
• Experience with autonomous vehicles, robotics, or other large-scale sensor data.
• Experience with observability for data systems, including metrics, alerting, and SLOs, and everyday use of AI coding agents.
• Backend or data-engineering experience with an interest in learning ML evaluation; ML training is provided.

Culture & Benefits

• Work alongside experienced engineers on complex self-driving and ML infrastructure problems.
• Values include integrity, ambitious goals, collaboration, safety, and inclusion.
• In-person work is expected at least 3 days per week, with some roles requiring more frequent office presence.
• Annual bonus, equity compensation, and employee benefits are provided.
• Reasonable accommodations are available for qualified individuals with disabilities and disabled veterans.