Senior ML Systems Engineer - Evaluation & AV Scale

General Motors; Mountain View (CA); Remote; USD 145,000 - 261,000; Full time

Benefits offered by this job

Health and wellbeing benefits

GM vehicle discounts

Tuition assistance

Job summary

General Motors is seeking a Senior Engineer for Embodied AI Scaling Foundations to measure and visualize AV model performance at scale. You will design and iterate evaluation tools across the stack, collaborating with modeling and data teams to connect signals to data, training, and launch decisions.

You will help deliver end-to-end evaluation workflows with model-based metrics, visualization, and dashboards, enabling faster experimentation and better safety-critical decisions in autonomous

Qualifications

• 3+ years of relevant industry experience in engineering or ML tooling.
• Experience with backend and data tooling including Python, data streaming, and scalable pipelines.
• Familiarity with visualization, dashboards, and model evaluation tools.

Responsibilities

• Build and improve evaluation signals and introspection tools to accelerate model development.
• Develop and maintain visualization and metrics within the model evaluation loop.
• Help teams introspect model behavior and derive actionable steps with fewer manual steps.
• Participate in design and code reviews and collaborate with Data, Infra, and validation teams.

Skills

3+ years experience

Strong communication

Python

React/TypeScript

WebGL/WebGPU

Jupyter notebooks

Education

Bachelor’s degree in Computer Science or related field

Tools

Python

React

TypeScript

WebGL

WebGPU

Streamlit

Jupyter notebooks

Spark

BigQuery

Kubernetes

Job description

General Motors is seeking a Senior Engineer for Embodied AI Scaling Foundations to measure and visualize AV model performance at scale. You will design and iterate evaluation tools across the stack, collaborating with modeling and data teams to connect signals to data, training, and launch decisions.

You will help deliver end-to-end evaluation workflows with model-based metrics, visualization, and dashboards, enabling faster experimentation and better safety-critical decisions in autonomous