Foundation EGI

Manage the full sales cycle for enterprise clients, including technical discussions, solutioning, pilot delivery, and closing. | Experience in enterprise sales, mechanical engineering knowledge, ability to handle technical customer conversations, and experience selling into manufacturing sectors. | About Us: We are an MIT-born, Series A startup building a real-life 'Jarvis'—an AI Copilot for design and manufacturing. Our goal is to utilize advanced AI, physics simulation, and computer graphics to reduce costs and improve engineering productivity across all steps of the design and manufacturing process. What You Will Do: Own the full sales cycle for Enterprise customers - from outbound prospecting to solutioning to closing and handoff. Help achieve quarterly customer and revenue targetsLead detailed technical conversations with the customers to understand their pain points. Translate them into customized sales proposals (POCs, pilots, commercial) with the requisite level of technical and commercial granularity. Own the delivery of the pilot projects for a very technical product ensuring that they successfully lead to a subscription. Be the voice of the customer with the internal product and R&D teams. Draft and communicate technical requirements for them to ensure your customer needs are correctly translated internally. Always looking for ways to improve the sales process and decrease the time to close. Mentor and hire future team members as we scale \n What You Will Need to Be Successful in the Role: Be extremely conversant with basic mechanical engineering concepts (e.g., GD&T, critical dimensions, tolerances, 3DCAD, technical drawings, etc.) and able to hold conversations with customers on these topics. Mechanical Engineering background will be a huge plus. 2-3 years of experience in Enterprise sales role selling into large design and manufacturing organizations. Selling into Automotive OEMs/Tier 1s and/or heavy equipment manufacturers is a plusAbility to learn fast, move fast and evolve quickly. Be a swiss-army knife. A knack for identifying the right stakeholders and champions within a large organization and building relationships with them. Navigate your way through layered, matrixed organizations. Handle objections and skepticism with confidence, especially when selling an emerging product that is not always polished. Ability to isolate customer pain points and translate them into a customized sales narrative for each customer. Ability to create urgency with the prospects and keep the deal moving Excellent communication skills with the ability to adapt on the fly and think on your feet. Ability to thrive in the fog of an early-stage startup environment and deliver resultsPerseverance, optimism and determination. Self starter. \n

Transform raw engineering data into structured datasets, create mechanical models and annotations, and collaborate cross-functionally to improve data quality for AI systems. | Strong domain expertise in mechanical engineering or manufacturing design, hands-on CAD experience, ability to interpret engineering drawings, and experience creating engineering artifacts. | We are an MIT-born, venture-backed Silicon Valley startup building Engineering General Intelligence (EGI)—an AI Copilot for design and manufacturing. Our mission is to fundamentally reinvent how physical products are designed and built, dramatically accelerating the pace of product development. As an Individual Contributor on the Data Studio team, you will play a key role in transforming raw customer data into structured, high-fidelity datasets that power model training, evaluation, and customer delivery. This role is deeply hands-on and sits at the intersection of product, research, and engineering. You will apply your mechanical engineering and manufacturing expertise to create data pipelines, labeling workflows, reference models, and quality checks that ensure the accuracy and reliability of our AI systems. Mechanical engineering or manufacturing design experience is essential; candidates without this background will not be considered. \n Key Responsibilities 1. Data Creation, Processing & Quality Ingest, clean, transform, and structure customer and internally generated engineering data for AI training and inference. Design and build high-quality mechanical components and assemblies in CAD to serve as authoritative ground truth for evaluating and training AI systems. Produce labeled datasets, reference designs, annotations, exploded views, sequences, and other engineering artifacts that encode real-world reasoning. Apply engineering judgment to define and assess output quality across datasets. Continuously refine standards for metadata, annotation, and model quality, maintaining a living “definition of quality” for ME datasets. 2. Workflow & Tooling Contributions Collaborate with Product Managers to shape tooling used for annotation, data correction, model-output review, and pipeline automation. Provide detailed feedback on tool usability, workflow efficiency, and automation opportunities. Help develop scalable, repeatable data processes that improve throughput and data consistency. 3. Cross-Functional Collaboration Partner closely with engineering and research teams to understand model data requirements, failure modes, and areas needing new data. Influence model behavior by supplying representative engineering examples and ground-truth mechanical designs. Partner with customer-facing teams to translate domain requirements, industry standards, and customer data schemas into actionable dataset specifications. Serve as a subject matter expert on mechanical engineering formats, CAD standards, manufacturing practices, and design artifacts. 4. Domain Expertise & Reference Content Creation Generate technical documentation, exploded views, sequences, and annotations that encode engineering reasoning into training data. Ensure that datasets reflect real-world constraints, DFM (Design for Manufacturing) considerations, material behavior, and industry best practices. Embed engineering reasoning into training data so that AI systems learn not just geometry or text, but engineering intent. 5. Customer & Project Support Work with customers to understand their data sources, schemas, formats, and quality expectations. Guide customers in preparing high-quality datasets, defining structured schemas, and improving data pipelines. Support delivery timelines by communicating progress clearly and surfacing risks or issues early. Review and work with external contractors, ensuring high-quality output and adherence to SOPs. Required Qualifications Strong domain expertise in mechanical engineering, manufacturing design, or industrial workflows. Hands-on experience with CAD tools such as SolidWorks, CATIA, Siemens NX, or Creo. Familiarity with annotation tools and illustration software (e.g., Creo Illustrate, Adobe Illustrator, Arbortext). Ability to interpret complex mechanical assemblies, technical drawings, GD&T, and engineering documentation. Experience creating artifacts like exploded views, work-step sequences, repair manuals, or manufacturing instructions. Strong problem-solving skills and the ability to translate domain workflows into structured data requirements. Excellent communication and cross-functional collaboration skills. Preferred Qualifications Experience with data operations, labeling workflows, ML data pipelines, or AI/ML data lifecycle (collection -> labeling -> QA -> training -> evaluation -> deployment). Experience in fast-paced startup or high-growth environments. Comfort with customer-facing discovery or solutioning. What Success Looks Like Deliver high-quality datasets that measurably improve model performance. Drive standardization and reliability across ME datasets, CAD models, workflows, metadata, and annotations. Enable faster model training, evaluation, and deployment through strong cross-functional collaboration. Maintain clear documentation, repeatable processes, and continuous quality improvement. Be recognized as a trusted ME expert in data quality and domain insight. \n

Design and develop algorithms for engineering applications, process engineering data, and contribute to AI and simulation tools. | Requires 5+ years in geometric processing, simulation, or AI, proficiency in Python, and experience with datasets and domain-specific languages. | We are an MIT-born, venture-backed Silicon Valley startup building a real-life 'Jarvis'. An AI Copilot for design and manufacturing. Our goal is to utilize advanced AI, physics simulation, and computer graphics to reduce costs and improve engineering productivity across all steps of the design and manufacturing process. \n Responsibilities Design, develop, and maintain geometry processing and simulation algorithms for engineering applications. Build services for reading, processing, and writing 2D/3D engineering data. Develop rendering modules for generating 2D/3D visual assets. Curate and manage large-scale datasets for learning-based systems. Implement and optimize post-training workflows for machine learning models. Contribute to the development of domain-specific languages for engineering tasks. What we are looking for 5+ years of academic or industry experience in one or more of the following areas: Geometric Processing, Simulation, Optimization, Machine Learning, or Domain-Specific Languages. BSc or MSc in Computer Science, Engineering, or a related field. Proficient in writing clean, modular, and maintainable Python code. Experience with dataset creation and data pipeline development. Bonus Points PhD or MS with a focus in Computational Design, Simulation, or AI. Experience developing CAD/CAM/CAE software tools. Experience developing or fine-tuning large language models (LLMs), including post-training methods such as quantization, pruning, distillation, or reinforcement learning. Experience designing or implementing DSLs or compilers. \n

Design and maintain Python backend services integrating CAD systems like CATIA with cloud-native platforms. | Requires 5+ years backend experience, 3+ years with CATIA or similar CAD APIs, Python proficiency, and cloud-native application knowledge. | We are a MIT-born, venture-backed Silicon Valley startup building a real-life 'Jarvis'—an AI Copilot for design and manufacturing. Our goal is to utilize advanced AI, physics simulation, and computer graphics to reduce costs and improve engineering productivity across all steps of the design and manufacturing process. We're looking for a Senior CAD Backend Engineer with strong CATIA experience to build backend integrations and automation for our AI Engineering platform. You'll connect CAD systems to cloud-native services, streamline mechanical design workflows and develop Python based backend features. CATIA background is a must. \n Responsibilities Design, develop, and maintain backend services and scripts using Python. Integrate our product with 3rd-Party design solutions, like Siemens NX, CATIA, or PTC. Design, develop, and maintain data models using Protobuf. Collaborate with cross-functional teams, including product managers, engineers and researchers. Write clean, well-documented, fast, and maintainable code. What we're looking for BS in Mechanical Engineering, Computer Science or a related field. 5+ years of experience designing and implementing backend services. 3+ years of experience with Siemens NX, CATIA, or PTC’s APIs. 3+ years of experience writing Python. Deep understanding of cloud-native applications and infrastructure. Experience working with Protobuf. Experience working with 3D data. Excellent written and verbal communication skills. Bonus Points Experience writing C++. Experience implementing gRPC-based APIs. Experience working with Docker. Experience working with Google Cloud. Experience setting up and maintaining CI/CD pipelines with GitHub Actions. Experience setting up logging and monitoring. Our tech stack Google Cloud Python, TypeScript Protobuf, gRPC Next.JS, React.JS GitHub Actions Docker, Kubernetes, Spinnaker PostgreSQL \n

Design, develop, and maintain backend services and data models integrating with 3rd-party design solutions. | 5+ years backend experience, 3+ years with Siemens NX/CATIA/PTC APIs and Python, BS in related field, cloud-native and 3D data experience. | Job Description: • Design, develop, and maintain backend services and scripts using Python. • Integrate our product with 3rd-Party design solutions, like Siemens NX, CATIA, or PTC. • Design, develop, and maintain data models using Protobuf. • Collaborate with cross-functional teams, including product managers, engineers and researchers. • Write clean, well-documented, fast, and maintainable code. Requirements: • BS in Mechanical Engineering, Computer Science or a related field. • 5+ years of experience designing and implementing backend services. • 3+ years of experience with Siemens NX, CATIA, or PTC’s APIs. • 3+ years of experience writing Python. • Deep understanding of cloud-native applications and infrastructure. • Experience working with Protobuf. • Experience working with 3D data. • Excellent written and verbal communication skills. Benefits:

Serve as subject-matter expert on automotive assembly planning and tooling, define product workflows and requirements, write specifications and documentation, and validate product behavior with real plant workflows. | 10+ years automotive manufacturing or industrial engineering experience, 5+ years in geometric processing or related fields, BSc/MSc in CS or Engineering, proficient in Python, and strong communication skills. | We are an MIT-born, venture-backed Silicon Valley startup building a real-life 'Jarvis'. An AI Copilot for design and manufacturing. Our goal is to utilize advanced AI, physics simulation, and computer graphics to reduce costs and improve engineering productivity across all steps of the design and manufacturing process. Overview Foundation EGI is looking for an experienced Manufacturing or Industrial Engineering Consultant with deep automotive experience in planning and documenting new production lines, especially final assembly operations.This person will serve as a domain expert for a new software product focused on assembly sequence and tooling planning, bringing real-world shop floor experience into the product design, requirements, and documentation.You should be an industry veteran who has led or supported the planning of new or modified production lines at OEMs or Tier 1 suppliers, including:Assembly sequence planningSelection and specification of assembly tools and equipmentTorque specification and tightening strategiesErgonomic risk assessmentMTM and time studiesProduction documentation and launch readiness \n Responsibilities Serve as the primary subject-matter expert on automotive assembly sequence planning, tooling, and production line setup. Define and review product workflows and requirements for sequence planning, torque specs, tooling selection, ergonomic risks, and MTM or time study use. Provide practical guidance on new or modified production lines, including station layout, work content, standardized work, and work instructions. Define and review how assembly tools, fixtures, ergonomic aids, and error-proofing devices are represented and used in the software. Write clear product specifications and user-facing documentation such as workflows, examples, and best-practice guides. Validate that the product behavior and algorithms match real plant workflows and provide feedback from pilot use and customer discussions. What we are looking for Must Have ten or more years of recent experience in manufacturing or industrial engineering roles within automotive OEM or Tier 1 suppliers. 5+ years of academic or industry experience in one or more of the following areas: Geometric Processing, Simulation, Optimization, Machine Learning, or Domain-Specific Languages. BSc or MSc in Computer Science, Engineering, or a related field. Proficient in writing clean, modular, and maintainable Python code. Experience with dataset creation and data pipeline development. You'll Thrive with Proven experience planning and launching new or significantly modified production lines, especially final assembly. Hands-on experience with: Assembly sequence planning and operation breakdown. Tool and equipment selection for assembly operations, including torque tools, fixtures, and ergonomic assists Torque specifications and fastening process design, including DC tools, tightening strategies, and traceability Ergonomic risk assessment using methods such as REBA, RULA, or NIOSHMTM, MOST, or equivalent time study methods Strong written and verbal communication skills and the ability to clearly explain complex manufacturing and tooling concepts to non-experts such as software engineers. \n

Design and develop algorithms and data processing services for engineering applications, focusing on geometry, simulation, and machine learning. | Requires 5+ years in geometric processing, simulation, or machine learning, proficiency in Python, and experience with datasets and data pipelines. | Job Description: • Design, develop, and maintain geometry processing and simulation algorithms for engineering applications. • Build services for reading, processing, and writing 2D/3D engineering data. • Develop rendering modules for generating 2D/3D visual assets. • Curate and manage large-scale datasets for learning-based systems. • Implement and optimize post-training workflows for machine learning models. • Contribute to the development of domain-specific languages for engineering tasks. Requirements: • 5+ years of academic or industry experience in one or more of the following areas: Geometric Processing, Simulation, Optimization, Machine Learning, or Domain-Specific Languages. • BSc or MSc in Computer Science, Engineering, or a related field. • Proficient in writing clean, modular, and maintainable Python code. • Experience with dataset creation and data pipeline development. Benefits:

Architect and operate ML pipelines on Google Cloud, maintain CI/CD for ML models, and collaborate with cross-functional teams to ensure high system availability and performance. | 5+ years in AI/ML Ops or related roles with expert Python and TypeScript skills, experience with Docker, Kubernetes, Terraform, Google Cloud, and knowledge of LLMs and CI/CD pipelines. | We are an MIT-born, venture-backed Silicon Valley startup building a real-life 'Jarvis'—an AI Copilot for design and manufacturing. Our goal is to utilize advanced AI, physics simulation, and computer graphics to reduce costs and improve engineering productivity across all steps of the design and manufacturing process. Responsibilities • Architect, build, and operate end-to-end ML pipelines for training, validation and deployment on Google Cloud. • Define, instrument, and maintain logging, monitoring, and alerting for model performance and data drift. • Automate CI/CD for ML artifacts and infrastructure using GitHub Actions or equivalent. • Collaborate with cross-functional teams, including frontend engineers, backend engineers, research engineers, and infrastructure engineers. • Write clean, well-documented, fast, and maintainable code. • Help ensure our systems have high availability and performance. What we're looking for • BS in Computer Science or a related field. • 5+ years of experience as a AI/ML Ops, DevOps, Infrastructure Engineer or equivalent. • Expert-level Python and TypeScripts skills. • Experience with Docker, Kubernetes, Terraform, and Google Cloud. • Deep understanding of large language models (LLMs) and prompt-engineering best practices. • Experience designing and maintaining CI/CD pipelines to fine-tune or train LLM models. • Excellent written and verbal communication skills. Bonus Points • Experience in computer graphics or physics-based simulation. • Background in setting up Prometheus/Grafana, ELK, or similar monitoring stacks. • Experience with Vertex AI. • Experience working with custom Domain-Specific Languages. Our tech stack • Google Cloud • Python, TypeScript • Protobuf, gRPC • Next.JS, React.JS • GitHub Actions • Docker, Kubernetes, Spinnaker • PostgreSQL