Senior Machine Learning Engineer - Hybrid (San Francisco or Austin)

Job Description

The Enterprise Machine Learning team drives organizational value through scalable ML solutions and data-driven insights, fundamentally changing how business decisions are made. We collaborate closely with stakeholders, applying the latest advances in machine learning, deep learning, and large language models (LLMs) to create highly impactful outcomes. Our commitment is to advance the state of AI, statistical modeling, and robust system design to enhance and expand our core business capabilities.

Location

San Francisco, CA or Austin, TX (Hybrid)

Schedule: This is a hybrid role requiring an average of 2 days per week in-office, or as otherwise determined by the hiring manager

Role Overview

As a Machine Learning Engineer, you will serve as a technical and strategic member within the team, driving the development and deployment of advanced data science and machine learning solutions—particularly those harnessing LLMs and deep learning. You will architect and scale ML systems, foster effective cross-functional collaborations, and ensure that business value is embedded in every technical decision. Your business acumen allows you to translate complex analytical approaches into actionable insights and stakeholder-friendly narratives, strengthening partnership and adoption across the enterprise.

Key Responsibilities

• Drive the design, development, and deployment of advanced ML and AI solutions, with an emphasis on large language models (LLMs), deep learning architectures, and sophisticated statistical modeling.

• Build scalable, robust data science systems—from data ingestion, data curation, data modeling to algorithm development, model deployment and monitoring—meeting enterprise-grade performance, reliability, and compliance standards.

• Act as a subject matter expert, collaborating with data scientists, ML engineers, analysts, and business stakeholders to understand needs, define requirements, and deliver practical solutions with measurable business impact.

• Effectively articulate complex technical concepts to non-technical partners, bridging gaps between technical teams and business operations for maximum results.

• Drive adoption of best practices in MLOps, including CI/CD pipelines, containerization, orchestration, observability, and reproducibility.

• Oversee and enhance the integrity, security, and compliance of all data science workflows and contracts.

• Stay abreast of the latest industry advancements in ML, LLMs, deep learning, cloud data engineering, and MLOps solutions (AWS, Kubernetes, Snowflake, etc.).

• Fostering technical excellence and ensuring alignment with business objectives.

What We’re Looking For

Education & Experience:

• 3+ years’ experience in Data Science, Machine Learning, or a related field

• BA/BS in Computer Science, Data Science, or related discipline (advanced degree is highly preferred)

Technical Expertise:

• Deep expertise in statistical modeling, machine learning, and deep learning (including practical experience with LLMs and transformers)

• Strong programming skills (Python preferred; Java, Scala, or similar also valued)

• Proven ability to build and optimize scalable data science solution, end-to-end from data pipelines (dbt, Astronomer, Snowflake, AWS) to deployment and monitoring (Docker, Kubernetes, CI/CD, MLOps best practices)

• Experience handling and analyzing large datasets, with a preference for experience in cloud data warehouses (Snowflake)

Business Acumen:

• Demonstrated success in translating business needs into analytical solutions, driving quantifiable impact

• Strong stakeholder engagement skills, with a track record of building trusted business partnerships and driving adoption of data science initiatives

Communication & Collaboration:

• Exceptional ability to simplify and communicate complex data science concepts to technical and non-technical a