About The Role

In this role you will help maintain and extend a web-based visual graph editor for manufacturing workflows. Users create and connect nodes representing materials/workpieces/tools and manufacturing steps (processes/inspections), validate the resulting workflow against catalogs and strict data contracts, and export a schema-compliant JSON representation for downstream tooling.

This role blends UI engineering with data integrity work. The editor must stay responsive on large graphs, preserve correctness (IDs, validation, export format), as well as support collaboration workflows through version control integrations.

This is a part-time, hybrid, freelance/contract role. Hours:

• 1st 60 days: 20-40 hours per week
• Ongoing: 10-20 hours (minimum) per week

What You’ll Do

• Build and maintain a React + TypeScript UI for a graph editor (node/edge creation, selection, multi-select, drag/drop, property panels)
• Implement schema-driven validation and export (JSON Schema / AJV), ensuring stable, backwards-compatible outputs as schemas evolve
• Improve performance and reliability for large graphs (rendering, minimizing re-renders, preventing UI jitter, safe persistence)
• Support workflows around identity/ID rules, including ID generation, transformations on copy/paste/import, and “minting/locking” behaviors that freeze certain fields once finalized
• Maintain integrations used in real production workflows: 
• GitLab API (branching, committing files, MR workflows)
• Methods API (catalog sync, offline fallback, validation that blocks pushes when API is down)
• Authentication (Okta)
• Ship small-to-medium features end-to-end: UI, state changes, validation, tests, and docs
• Triage bugs from real users (manufacturing/process engineers), reproduce quickly, and implement safe fixes
• Add/maintain unit tests (Jest + TS) around tricky logic (ID transformation, export validation, API sync behavior)
• Keep the build/lint/test pipeline healthy (Vite, TypeScript, ESLint, Jest) and handle dependency upgrades carefully
• Collaborate asynchronously with a cross-functional team (process, production, AI) through clear PRs, strong commit hygiene, and pragmatic technical decisions
• Tools/Stack You’ll Work With: React 18, TypeScript, Vite, Tailwind CSS, ReactFlow, AJV (JSON Schema), Jest, ESLint, GitLab API, OpenAPI, Okta

What You’ll Need

• Experience building complex, stateful UI systems (not CRUD/dashboard apps)
• Strong React + TypeScript with state management + performance optimization
• Experience with graph/diagram editors or similar interaction-heavy UIs
• Experience with large in-memory data + UI/data synchronization
• Experience with validation systems + deterministic outputs (schema, exports)
• Strong debugging in complex systems + ability to explain tradeoffs
• Experience leading complex, ambiguous problems across cross-functional teams with multiple stakeholders

Bonus Points For:

• Manufacturing background (MES, process planning, CAD/CAM adjacent tooling, “recipes/methods/catalog” concepts)
• JSON Schema / AJV experience and comfort with strict data contracts
• GitLab API experience, auth systems (Okta/OIDC), or “internal tool” deployment constraints

About The Role:

We are seeking an L4 Modeling and Simulation Engineer to join our growing AI and Modeling & Simulation team. The ideal candidate has experience developing and deploying mathematical and numerical models across multiple coupled physics domains. In this role, you will identify the critical attributes of existing manufacturing systems and future machine concepts, then use those insights to build, simulate, and deploy parameterized models that support machine design and optimization.

To succeed in this role, you should have experience with multiscale and multiphysics modeling and be comfortable using simulation tools such as COMSOL, Ansys, or similar platforms. You should also have a strong foundation in structural mechanics, heat transfer, and CFD. Familiarity with other simulation domains, such as electromagnetics or electrochemistry, is a plus.

This is an excellent opportunity for someone with a track record of building high-quality, validated multiphysics models and who is excited to apply those skills to support the development of new classes of devices manufactured at scale on Atomic’s platform. The ideal candidate thrives in a collaborative environment and is effective at documenting, communicating, and presenting technical work.

What You’ll Do:

• Identify critical system attributes and develop computational models for current and future manufacturing machines, with an emphasis on structural, thermal, and fluid systems.
• Define figures of merit, boundary conditions, and operating conditions based on system requirements and cross-functional input.
• Build and deploy multiphysics simulations, including thermal, mechanical, and fluid models, using finite element and/or finite volume methods for both steady-state and transient analyses.
• Deliver executable computational models that support sensitivity analysis, design exploration, and optimization.
• Propose validation experiments and use experimental results to calibrate, validate, and improve models.
• Document simulation methods, assumptions, and validation results, and communicate findings clearly to the broader team.
• Collaborate cross-functionally with prototype engineers, designers, and AI engineers.

What You’ll Need:

• 4+ years of hands-on experience in multiphysics modeling and simulation following completion of a bachelor’s degree (or recent PhD).
• Strong foundation in multiphysics simulation, with particular depth in structural mechanics, heat transfer, and fluid dynamics.
• Programming experience, especially in Python, and experience automating simulation workflows for design and optimization.
• Experience with COMSOL and/or Ansys; experience with Abaqus, Siemens tools, or related software is a plus.
• Strong written and verbal communication skills, with the ability to work independently and collaboratively.
• Resourceful, flexible, and adaptable approach to technical work.
• Bachelor’s, master’s, or PhD in engineering, physics, or a related field.

Bonus Points For:

• Experience with CAD tools such as Onshape, Fusion 360, SolidWorks, NX, or similar.
• Experience with data analysis and visualization tools such as JMP, MATLAB, pandas, or matplotlib.
• Industry experience with product development processes such as Agile or similar frameworks.

About The Role:

What You’ll Do:

• Build CAD workflows that programmatically construct, modify, and evaluate 3D assemblies. This includes:
  • Manipulating and parametrizing CAD models to support iterative design and optimization.
  • Integrating with CAD APIs to move beyond GUI-based workflows, enabling high-throughput automated modeling and simulation.
  • Developing geometric intelligence — algorithms that infer how parts mate, fit, and interact to form functional assemblies.
• Create and maintain a library of parametric components for generative design exploration.
• Develop GenAI-driven workflows for CAD generation and automated design exploration.

What You’ll Need:

Required

• Deep expertise in CAD manipulation and parametrization, including scripting and automation (e.g., with Onshape API, SolidWorks API, Fusion API, or similar).
• Strong background in programmatic CAD workflows.
• Knowledge of AI-based CAD generation or generative design algorithms.
• Deep understanding of CAD interoperability standards (STEP, IGES, JT, or similar).
• Proficiency in interacting with CAD software via APIs (Python, C++, or other relevant languages).
• Experience in parsing large sets of CAD models with product and manufacturing information to create batchable datasets for training.
• Bachelor’s or Master’s or PhD degree in Mechanical Engineering, Aerospace Engineering, Computational Design, Computer Science, or a related field.
• For Staff level, 10+ years of industrial experience (post Bachelor’s), or 8+ years of industrial experience (post Master’s) or 5+ years of industrial experience (post PhD), with the option of relaxing these requirements for a Senior level position.

Nice to have

• Experience with geometric modeling kernels (e.g., OpenCascade, Parasolid).
• Experience in mastering NURBS and B-Rep.
• Experience with meshing, especially for simulation or manufacturing purposes.
• Experience with Model-Based Definitions.

Bonus Points For

• Experience with implicit geometric representations.
• Experience with simulation-in-the-loop design, shape optimization, or topology optimization workflows.

About The Role:

We are seeking a Senior Data Engineer to join our growing team within the AI and Modeling and Simulation 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, Design, 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. The ideal applicant thrives working in a cross-functional environment, unifies and integrates efforts of a highly diverse team, actively engages in the development process, and is excellent at documenting and presenting work products.

What You’ll Do:

• Data Engineering & Infrastructure
  • Design, build, and maintain scalable workflows for data collection, transformation, and storage.
  • Process and analyze structured or semi-structured manufacturing data.
  • Develop data pipelines to process and prepare data for ML model training and data analysis.
  • Develop, customize, and maintain interactive dashboards, reports, and visualizations for experimentation results and business metrics.
  • Ensure real-time data handling for ML applications (e.g., digital twins) and process monitoring.
• Validation & Quality Control
  • Work with inspection engineers/technicians to collect datasets and collaborate with process engineers to ensure quality control in manufacturing processes.
  • Experience with real-time data processing frameworks (Apache Kafka, Spark Streaming, etc.)
  • Experienced with BI toolsets and data-visualization frameworks (including Apache Superset) for reporting and analytics.
    • Build 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.
• Documentation & Collaboration
  • Work with process engineers on 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 AI engineers, to optimize data workflows and improve operational efficiency.

What You’ll Need:

• 6+ years after Bachelor’s / 4+ years after Master’s of industry experience. 
• A first-principles mindset — you question assumptions, reframe problems from the ground up, and approach challenges with a foundational understanding rather than relying solely on precedent.
• Proven experience in data engineering, data flows, and big-data processing.
• Proficiency in Python and programming languages such as SQL.
• Proficiency in data storage solutions (Data Lakes, Cloud Storage, SQL, NoSQL).
• Understanding of manufacturing processes, sensors, and process automation.
• Industry-level experience in guiding and automating data collection and processing in manufacturing environments.
• Knowledge of DevOps practices. 
• Strong problem-solving skills and ability to work in a collaborative, fast-paced environment.
• Strong analytical mindset, attention to data quality, and ability to translate complex data into clear insights for both technical and non-technical stakeholders.
• Bachelor’s or Master’s degree in Computer Science, Data Engineering, Data Science, or a related STEM field.

Bonus Points For:

• Experience with real-time data processing frameworks (Apache Kafka, Spark Streaming, etc.) 
• Experienced with BI toolsets and data-visualization frameworks (including Apache Superset) for reporting and analytics.