MLOps Engineer at atomicmachines

Compensation

$200,000 – $250,000/yr

Location: Emeryville, California
Type: Full-time
Level: senior
Posted: Sep 3, 2026

Description

Atomic Machines is hiring an MLOps Engineer to build and operate production machine-learning infrastructure for its AI and modeling and simulation organization. The role covers model experimentation, training, deployment, serving, monitoring, data and feature pipelines, lakehouse and observability systems, and the feedback loop from production signals back to model retraining. It requires senior-level experience, strong DevOps and Python/SQL skills, and familiarity with MLflow, S3, Apache Iceberg, Airflow or Dagster, and multimodal data. The position is open across multiple career levels and offers a salary range of $200,000 to $250,000 USD, plus equity and benefits.

Required Skills

MLOps
CI/CD
Kubernetes
Python
SQL
MLflow
Apache Iceberg
Airflow
Dagster
Model observability

Additional Information

Minimum Education: Bachelor's or Master's degree in Computer Science, Data Engineering, Data Science, or a related STEM field, or equivalent practical experience.
Required Previous Experiences: 5+ years of relevant industry experience building production software, infrastructure, data, or machine learning systems.
Experience building and operating machine learning systems in production with a strong MLOps/DevOps orientation.
Hands-on experience with MLflow or similar tooling for experiment tracking, model registry, and model lifecycle management.
Experience with S3, Apache Iceberg, and workflow orchestration tools such as Airflow or Dagster.
Experience building pipelines for multimodal machine learning data.
Familiarity with manufacturing systems, sensors, process automation, or other physical-world data systems.
Preferred Qualifications: Experience with feature stores, human-in-the-loop systems, active learning, or data-labeling infrastructure.
Robotics or robotic automation experience, including sensors, vision systems, or robotics data.
Experience operating machine learning systems in manufacturing or other physical-world environments.
Experience building internal tools for expert feedback, labeling, model evaluation, or model interaction.
Experience designing shared machine learning infrastructure or platforms used across multiple teams or applications.