Senior Machine Learning and Artificial Intelligence Scientist

General Motors (GM)

Confirmed live yesterday High trust
Hybrid

Quick summary

Work type
Hybrid
Location
Austin, TX
Salary
$159,800–$244,300 / yr
Posted
11 days ago
Freshness
Confirmed live yesterday

Market check

Salary context

Competitive pay

How this pay compares to similar roles

Similar $205k
This role $202k
$149k most similar roles pay here $259k

This role pays less than 51% of similar roles. Most pay $162,000–$248,375 — the shaded band above. At the midpoint, this role pays about $202k versus about $205k for comparable roles.

Based on 240 similar postings.

Employer

About General Motors (GM)

General Motors (GM) is a leading American multinational automotive corporation founded in 1908 and headquartered in Detroit, Michigan.

General Motors (GM) currently has 116 open roles on FindRole.

Listed pay typically runs $160,200–$245,000 across 59 roles with salary data.

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View all roles at General Motors (GM)

At a glance

TL;DR · Senior Machine Learning and Artificial Intelligence Scientist

The Senior Machine Learning and Artificial Intelligence Scientist leads the development and production deployment of advanced ML and AI solutions designed to deliver measurable business impact. This role involves transforming ambiguous business objectives into well-defined analytical problems, designing multi-agent systems, and building robust data pipelines for forecasting, NLP, and computer vision. The candidate will work with complex, imperfect data structures while collaborating with cross-functional teams to build scalable products. Technical expertise includes Python, SQL, PySpark, and frameworks like TensorFlow and PyTorch. The role requires proficiency in Azure, Databricks, AWS, and Google Cloud Platform environments. Key technologies include MLflow, Spark, Delta Lake, and various LLM tools such as retrieval-augmented generation and vector search. The position focuses on solving complex problems involving high-volume data, ensuring model safety, and implementing MLOps and LLMOps practices for reliable production deployment.

What you'll do

  • Translate ambiguous business objectives into well-defined analytical problems with measurable success criteria and deployment strategies.
  • Design, develop, and deploy production-grade machine learning models for tasks like forecasting, NLP, and computer vision.
  • Build and deploy generative AI and multi-agent systems using LLMs, RAG, and automated workflow engines.
  • Engineer robust data and feature pipelines using batch and streaming patterns while ensuring data quality and lineage.
  • Architect scalable cloud-based AI solutions across Azure, Databricks, AWS, and Google Cloud Platform environments.
  • Implement MLOps and LLMOps practices for model versioning, automated testing, drift detection, and performance monitoring.
  • Establish evaluation frameworks to measure factuality, safety, cost, and other critical business impact metrics.
  • Communicate technical findings, risks, and architecture decisions to non-technical stakeholders in clear, decision-oriented language.

What we're looking for

  • Bachelor’s degree in Computer Science, Data Science, Statistics, Mathematics, Engineering, or a related technical field; advanced degree preferred.
  • 5+ years of experience developing and deploying machine learning or artificial intelligence solutions in production environments.
  • Proven success delivering ML or AI solutions that generated measurable business impact such as improved accuracy, reduced cost, or increased revenue.
  • Proficiency in Python, SQL, PySpark, and common machine learning frameworks like TensorFlow, PyTorch, scikit-learn, and XGBoost.
  • Experience with modern generative AI architectures including LLMs, RAG, vector search, prompt engineering, and agent orchestration.
  • Expertise in cloud platforms including Microsoft Azure (Databricks), Amazon Web Services (SageMaker), and Google Cloud Platform (Vertex AI).
  • Strong knowledge of production engineering practices including Git, CI/CD, Docker, Kubernetes, infrastructure as code, and API design.
  • Ability to communicate technical concepts, risks, and architecture decisions to non-technical stakeholders and executive leadership.

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