Atomic Machines is ushering in a new era in micromanufacturing with its Matter Compiler (MC) technology. The MC enables new classes of micromachines to be designed and built by offering manufacturing processes and a materials library that is inaccessible to semiconductor manufacturing methods. The MC promises to unlock MEMS manufacturing both for the many device classes that never could be made by semiconductor methods but also to open up entirely new classes. Furthermore, the MC is fully digital in the way 3D printing is digital, but where 3D printing produces parts of a single material using a single process, the MC is a multi-process, multi-material technology: bits and raw materials go in and complete, functional micromachines come out. The Atomic Machines team has also created an exciting first device – one that was only made possible by the existence of the Matter Compiler – that we will be unveiling to the world soon.
Our offices are in Berkeley and Santa Clara, California.
About the role:
We are seeking a Staff Data Engineer to join our growing team within the AI and ModSim group and support manufacturing processes on our robotic manufacturing platform. The ideal candidate will be responsible for designing, implementing, and maintaining robust data pipelines and infrastructure to ensure the availability, integrity, traceability, and interpretability of manufacturing data. This role involves working closely with process designers and AI engineers to validate manufacturing outcomes and monitor process performance through data-driven insights.
Experience in data engineering, data flows, and big-data processing as well as proficiency in Python are essential for this role. Importantly, candidates will be expected to have an industry-level understanding of manufacturing processes and sensors, and to be able to work with process engineers on metrology needs and hardware requirements. Experience in data science for manufacturing – e.g., building data-driven predictive pipelines using statistics- and/or ML-based methods – is desirable.
This is an excellent career opportunity for a professional with a proven track record of creating and deploying data pipelines in manufacturing environments and who is excited about increasing their level of responsibility from Data Engineer to owning data at Atomic Machines. The ideal applicant thrives working in a cross-functional lead role, unifies and integrates efforts of a highly diverse team, actively engages in the development process, and is excellent at documenting and presenting work products.

• Data Engineering & Infrastructure:
• Design, build, and maintain scalable data collection, transformation, storage, and integration systems across different manufacturing units and processes.
• Develop data pipelines to process and prepare data for ML model training and real-time analytics.
• Ensure real-time data handling for ML applications and process monitoring.

Validation & Quality Control:

• Work with inspection engineers/technicians to collect datasets and collaborate with process designers and developers to ensure quality control in manufacturing processes through data validation.

• Guide the creation of validation tests to compare in-house inspection algorithms with commercial tools.

Process Monitoring & Optimization:

• Establish process monitoring frameworks by creating data storage solutions and implementing structured labeling for process input parameters and associated outputs.
• Develop online statistics and analytics for process control and optimization.
• Analyze observable time-series data corresponding to different manufacturing process stages.

• Derive required datasets, assess data availability, and select appropriate data collection systems to improve data quality and process efficiency.

Documentation & Collaboration:

• Work with process development engineers to propose data collection requirements and communicate those requirements to hardware designers.
• Collaborate with cross-functional teams, including process engineers, chemical engineers, materials scientists, simulation engineers, software developers, data scientists, and ML engineers, to optimize data workflows and improve operational efficiency.