Senior AI/ML Engineer: Enterprise AI Workflows & Equity

We Love AI Jobs; San Francisco (CA); On-site; USD 240,000 - 270,000; Full time

Benefits offered by this job

Equity

Generous health benefits

Flexible time off policy

Paid bonding time for new parents

Traditional and Roth 401k

Commuter benefits

Lunch Program

Job summary

Sigma Computing seeks a Senior AI/ML Engineer in San Francisco to build and productionize AI systems for the analytics platform, including recommendations, natural-language interfaces, and agentic workflows. You will own the ML lifecycle from data curation to deployment and monitoring.

You will collaborate with product, design, and engineering, scale AI infrastructure for internal tools and customer-facing features, and work on foundation models.

Qualifications

• Bachelor’s degree in Computer Science, Engineering, Mathematics, or related field.
• 10+ years of experience building and deploying production AI/ML systems.
• Experience across the full ML lifecycle: data curation, training, deployment, monitoring.

Responsibilities

• Identify high-impact AI/ML opportunities with product, design, and engineering teams.
• Prototype and productionize AI systems such as recommendations, natural-language interfaces, and agentic workflows.
• Develop and scale AI/ML infrastructure for internal tooling and customer-facing features.
• Tackle novel UX problems at the intersection of AI, BI, and apps.

Skills

Machine learning

Deep learning

Applied AI

Data curation

Model training

Model deployment

Model monitoring

Cloud platforms

Education

Bachelor's degree in Computer Science, Engineering, Mathematics, or related field

Tools

AWS

GCP

Azure

Job description

Sigma Computing seeks a Senior AI/ML Engineer in San Francisco to build and productionize AI systems for the analytics platform, including recommendations, natural-language interfaces, and agentic workflows. You will own the ML lifecycle from data curation to deployment and monitoring.

You will collaborate with product, design, and engineering, scale AI infrastructure for internal tools and customer-facing features, and work on foundation models.