What are the responsibilities and job description for the Founding Software Engineer position at MeshOS AI?
About Us
We are making an AI teammate for hardware engineers to help hardware teams make hardware faster and cheaper. We have raised a significant amount from established institutional VC firms and are working to disrupt the hardware design industry. Our first goal is to empower manufacturers and product designers with an autonomous (or semi-autonomous) “mechanical engineer”. If you’re excited by the idea of building an AI mechanical engineer that shortens product design cycles, lowers manufacturing costs, and changes how the world thinks about mechanical R&D, we look forward to meeting you!
About the Role
As the Founding Software Engineer you will be the primary Tech Architect guiding the end-to-end system architecture. You’ll define how ML components, microservices, data pipelines, and user-facing interfaces come together to deliver real-world design automation. You will collaborate with the ML / AI engineering team, front-end developers, and mechanical domain experts to ensure the system can handle large engineering datasets, and manage advanced AI models in production. You will also own the architectural blueprint and ensure the final product meets performance, reliability, and security benchmarks. As a founding team member, you will also be a key enabler of the engineering and company culture and processes.
A few key responsibilities would include one or more of the below :
- System Architecture & Design
- Drive the overall software architecture for our AI mechanical design platform—choosing frameworks, messaging patterns, data storage solutions, and compute infrastructure.
- Outline microservice boundaries (e.g., geometry service, AI inference service, CAD / Sim integration services) and communication protocols (REST, gRPC, message queues).
- Scalability & Reliability
- Ensure the platform can handle large-scale geometry data and concurrent AI inference tasks.
- Define strategies for load balancing, container orchestration (Docker / Kubernetes), and HPC integration if needed.
- Implement fault tolerance and monitoring to guarantee uptime for manufacturing or engineering teams.
- DevOps & CI / CD
- Collaborate on CI / CD pipelines for rapid deployment of AI models and software updates.
- Set up infrastructure-as-code (e.g., Terraform, AWS CloudFormation) to standardize environment provisioning.
- Oversee version control for both ML models (MLflow, DVC) and application code.
- Security & Data Governance
- Establish secure handling of IP-sensitive files, possibly using encryption at rest and in transit.
- Implement role-based access control, secure API gateways, and compliance with relevant standards (e.g., ISO for manufacturing data).
- Performance Optimization
- Identify bottlenecks in geometry parsing, AI inference, or simulation invocation.
- Work with ML engineers to implement model serving best practices (batch inference, GPU-based HPC, or serverless CPU / GPU scaling).
- Optimize data pipelines for reading / writing large files.
- Technical Leadership & Mentorship
- Guide the engineering team on best architectural patterns (microservices vs. monolith, event-driven vs. synchronous, etc.).
- Promote clean code, modular design, and domain-driven architecture that suits advanced AI workflows.
- Support debugging complex issues that span multiple subsystems (from AI code to storage layers).
Skills & Qualifications
Why Join Us
What We Offer
How to Apply
If you're passionate about AI and eager to make a significant impact from the ground up, we'd love to hear from you. Please send your CV / LinkedIn plus any relevant code or ML project links (GitHub, portfolio) to meshos@meshos.ai. In your cover letter, we’d love a brief overview of any experience you have combining AI with 3D geometry or engineering workflows.
