AI/ML Engineer - Remote Eligible

Senior Machine Learning Engineer

This role is based remotely but if you live within a 50-mile radius of an office [Atlanta, Austin, Detroit, Warren, or Mountain View], you are expected to report to that location three times a week, at minimum.

The Role

General Motors' Marketing Applied Sciences (MAS) team is seeking an experienced, technical-oriented, impact-delivering expert in Gen AI and machine learning with a strong ability to execute hands-on technical work. In this role, you will be responsible for designing and building scalable, reliable, and high-performance AI/ML products to support key business initiatives. As a Senior Machine Learning Engineer, you will collaborate closely with product managers, data engineers, data scientists, and other partners to develop state-of-the-art AI solutions that enable the future of marketing. This role sits within the Enterprise Data, Analytics, & Insights (EDAI) organization and plays a critical part in transforming GM's data into actionable, personalized experiences.

Success in this role requires a blend of technical aptitude, marketing know-how, and cross-functional collaboration. This role is ideal for someone who thrives at the intersection of data science and marketing strategy, and who is eager to shape the future of customer experiences at one of the world's most iconic automotive brands.

What You'll Do

• Design, build, and deploy autonomous AI agents using LLMs and agentic architecture frameworks.
• Manage memory, tools, goals, and execution environments.
• Build interfaces between agents, internal data systems, RAG pipelines, and cloud-based services.
• Collaborate with internal teams and stakeholders to rapidly prototype and iterate on novel agent capabilities.
• Demonstrate software engineering (SWE) skills, focusing on distributed backend development, batch data processing.
• Experiment and learn the latest AI development.
• Elevate system design, diagnostics, and operational excellence to higher standards.
• Collaborate with cross-functional teams to integrate new features and technologies into the platform.

Your Skills & Abilities (Required Qualifications)

Bachelors or higher degree in Computer Science or equivalent major or equivalent experience.

5+ years professional software engineering or machine learning engineering experience.

5+ years specialized experience in AI/ML infrastructure, e.g., enabling distributed training for scaling large ML models.

Strong programming skills in Python, with proficiency in frameworks such as PyTorch (preferred).

Experience building autonomous agents using frameworks like CrewAI, Agno, LangChain Agents, Autogen.

Deep understanding of LLM internals (prompting, tokenization, inference, function calling).

Experience integrating multiple data sources and orchestrating multi-step agent workflows.

Familiarity with vector databases and search retrieval techniques.

Experience with distributed computing, GPU computing, and cloud environments (AWS, GCP, Azure).

Demonstrated ability to lead projects that bridge marketing, data science, and technology to drive measurable outcomes.

Ability to simplify complex data strategies into actionable marketing solutions and communicate technical concepts to non-technical stakeholders.

Strong collaborative mindset and experience working with cross-functional teams including marketers, engineers, data scientists, and agency partners.

What Will Give You a Competitive Edge (Preferred Qualifications)

• Strong experience with Databricks for large-scale data engineering, model training, and serving in production environments.
• Proven ability to optimize ML workloads for performance and cost within Azure-based cloud architecture.
• 5+ years of experience running AI/ML solutions in production, including monitoring, retraining, and drift detection.
• Deep familiarity with MLflow, ideally MLflow 3.0, including model tracking, versioning, and governance.
• Working knowledge of MLOps best practices, including CI/CD for ML, model registry usage, and reproducible workflows.
• Deep understanding of marketing data ecosystems, including online/offline data sources, clean rooms, onboarding solutions, and ID resolution.