Engineering Manager — Eval & ML Data Pipelines

Polluxa, Inc.; Mountain View (CA); Hybrid; USD 180,000 - 260,000; Full time

Job summary

Polluxa, Inc. seeks an experienced engineering manager to lead a data-science‑friendly team focused on evaluating safety and ML development across new cities. The role blends hands-on coding with leadership, requiring strong quantitative skills and a track record of building data pipelines.

You will partner with product, engineering, and analytics teams to scale evaluation methods, mine data from logs, and develop automated tooling for rapid, safe expansion.

Qualifications

• BS/MS in a quantitative field; strong foundation in CS, robotics, statistics or math.
• 7+ years of industry experience in ML/data-intensive roles.
• Experience leading teams of up to 8 engineers/scientists.
• Proficient in C++, SQL and Python; ability to navigate large code bases and build data pipelines.

Responsibilities

• Develop tools for mining novel situations from driving logs for evaluation and ML datasets.
• Apply sampling and simulations to generate strong signals for metrics and models.
• Build automation for mining and sampling ecosystems to scale to new cities.
• Contribute hands-on technically to engineering and data science projects.
• Provide engineering leadership for a data-science friendly team of engineers.
• Collaborate with product, analytics, frontend and backend to set strategy and execution.

Skills

C++

SQL

Python

Data analysis

ML concepts

Team leadership

Experiment design

Statistics

Education

BS in Computer Science / Robotics / Statistics / Math

MS in a quantitative field

Job description

Polluxa, Inc. seeks an experienced engineering manager to lead a data-science‑friendly team focused on evaluating safety and ML development across new cities. The role blends hands-on coding with leadership, requiring strong quantitative skills and a track record of building data pipelines.

You will partner with product, engineering, and analytics teams to scale evaluation methods, mine data from logs, and develop automated tooling for rapid, safe expansion.