Senior MLOps Engineer: Production ML Platform

Atomic Machines; Emeryville (CA); On-site; USD 200,000 - 250,000; Full time

Job summary

Atomic Machines is seeking an experienced MLOps Engineer to join the AI and Modeling & Simulation org within Data Engineering and Analytics. You will build and operate infrastructure that takes AI models from experimentation to reliable production, covering training, deployment, serving, monitoring, and continuous improvement.

You will collaborate with Data, AI, Process, Design, and Software engineers in a cross-functional environment, taking ownership of production ML infrastructure with

Qualifications

• Proven experience building production ML systems with MLOps/DevOps orientation.
• Strong DevOps fundamentals including CI/CD, containers, and IaC.
• Experience with multi-modal ML data pipelines and data quality.

Responsibilities

• Build and evolve the MLOps platform and CI/CD for experiment-to-production flow.
• Operate model serving infrastructure for batch, streaming, and real-time inference.
• Create ML data and feature pipelines from telemetry, graphs, time-series, images, and conversations.
• Maintain ML data infrastructure with lakehouse tech using Apache Iceberg on S3.
• Close the model feedback loop with observability and human-in-the-loop systems.
• Develop tooling and workflows enabling rapid, reliable ML development and production reliability.

Skills

Python

SQL

Problem solving

Technical communication

Education

Bachelor's or Master's in CS/Data Eng/DS or related STEM

Tools

MLflow

Airflow

Dagster

Kubernetes

Docker

S3

Apache Iceberg

Job description

Atomic Machines is seeking an experienced MLOps Engineer to join the AI and Modeling & Simulation org within Data Engineering and Analytics. You will build and operate infrastructure that takes AI models from experimentation to reliable production, covering training, deployment, serving, monitoring, and continuous improvement.

You will collaborate with Data, AI, Process, Design, and Software engineers in a cross-functional environment, taking ownership of production ML infrastructure with