MLOps Engineer
Atomicmachines · Emeryville, California
About this role
**Atomic Machines — MLOps Engineer** Atomic Machines is ushering in a new era of micromanufacturing with its **Matter Compiler™** technology platform. This platform enables new classes of micromachines by providing manufacturing processes and a materials library that are inaccessible to semiconductor manufacturing methods. We’re seeking a **Senior-level MLOps Engineer** to join our **AI and Modeling & Simulation** org within the **Data Engineering and Analytics** team. You’ll build and operate infrastructure that takes AI/ML models from experimentation to reliable production—covering **training, deployment, serving, monitoring, and continuous improvement**. This is a **DevOps-leaning MLOps** role centered on the **model feedback loop**, connecting production signals and expert feedback back to training so models improve as the system operates. **Location:** Emeryville and Santa Clara, California. --- ## What You’ll Do - **Build and evolve the MLOps platform and CI/CD:** Own the path from experiment to production (experiment tracking, model registry, packaging, automated training/retraining, deployment, safe rollout/rollback). - **Operate model serving infrastructure:** Build reliable, scalable **batch, streaming, and real-time inference** for models and **digital twins** supporting design, process control, scheduling, and inspection. - **Build ML data and feature pipelines:** Turn machine telemetry, process/knowledge graphs, images, time-series, agentic conversations, and other production data into model-ready datasets and features. - **Maintain ML data infrastructure:** Support **feature-store** capabilities and a **lakehouse** foundation using **Apache Iceberg on S3**, with strong data quality, lineage, versioning, and reproducibility. - **Close the model feedback loop:** Build model observability and human-in-the-loop systems that capture production signals and expert corrections, version them as ground truth, and feed them into evaluation and retraining workflows. - **Create paved roads for ML development:** Develop standardized tooling/workflows that help Data and AI engineers move quickly while maintaining production reliability and reproducibility. - **Drive technical ownership:** Identify infrastructure/reliability/scalability challenges and drive solutions from design through production (more senior candidates may shape architecture and engineering practices across the ML platform). - **Collaborate across disciplines:** Work with Process, Chemical, Materials, Simulation, Software, Data, and AI engineers to define deployment, serving, and data-collection requirements. --- ## What You’ll Need - **5+ years** building production software/infrastructure/data/ML systems (we value demonstrated depth, ownership, and impact). - Proven experience building and operating **ML systems in production** with a strong **MLOps/DevOps** orientation. - Strong DevOps fundamentals: **CI/CD, containers, Kubernetes, cloud infrastructure, infrastructure-as-code**. - Proficiency in **Python** and **SQL**. - Hands-on experience with **MLflow** (or similar) for experiment tracking, model registry, and lifecycle management. - Experience with **S3**, **lakehouse technologies** like **Apache Iceberg**, and orchestration tools such as **Airflow** or **Dagster**. - Experience building pipelines for **multimodal ML data** (images, time-series, structured, semi-structured). - Familiarity with **manufacturing systems**, sensors, process automation, or othe
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