About Us
Our mission is to unlock a new level of scalability of our civilization by automating mechanical design processes using AI agents. We are a rapidly growing startup that raised a seed round in April 2026.
We are building a world where low-level design implementations are managed by AI agents, enabling every designer’s vision to transcend minute details. Our product will unleash creativity and insight, no longer constrained by the complexities of design. The upper limit of mechanical, topological, and physical complexity that can be managed by our civilization will expand—faster than ever before in human history.
Now, it is your mission to define what this world should look like.
Research Engineer, Mechanical Intelligence
In this role, you will:
- Build agentic data-generation pipelines for high-fidelity, manufacturing-grade synthetic designs with embedding-friendly representations for neural network training.
- Build modular tool interfaces and execution protocols for agentic design systems, spanning structural, fluid, heat-transfer, and aerothermodynamic analysis, as well as synthetic geometry generation through automated CAD operations.
- Build and train state-of-the-art mechanical design agents that translate imagination into real-world designs by autonomously characterizing physical elements and governing principles.
- Collaborate with industry experts across technical disciplines and geographies around the world to improve the efficiency, reliability, and quality of design agents.
What We’re Looking For
Mindset:
- Outlier builder with exceptional velocity.
- Exceptional resilience, ownership, initiative, and a problem-solving mindset, even under immense pressure.
- Willingness to work extended hours, including weekends, when necessary to meet critical deadlines.
- Outstanding teamwork in tackling interdisciplinary engineering challenges alongside experts from diverse backgrounds.
Must-have:
- First-principles critical thinking across thermal, fluid, structural, and dynamic systems.
- Demonstrable achievements in characterizing complex physical problems by identifying governing parameters and establishing appropriate reduced-order models.
- Exceptional learning and execution speed from concept to implementation.
- Proficiency in Python.
Nice-to-have:
- Proficiency in LLM-based development environments (e.g., Claude Code multi-agent development workflows).
- Expertise in numerical analysis or design optimization (e.g., a master’s or Ph.D. focused on thermal-fluid solver development, computational mechanics, or structural design optimization).
- Familiarity with AI frameworks (e.g., hands-on experience with fine-tuning language models, designing transformer architectures, or training machine vision networks).
- Expertise in computer-aided design (CAD) software (e.g., CATIA, NX, or FreeCAD).
- Familiarity with batch processing using shell scripts in cloud environments (e.g., GCP or AWS).
Machine Learning Research Engineer, Mechanical Intelligence
In this role, you will:
- Build and improve the core model architecture behind mechanical design agents, including transformer-based, vision-based, and multimodal models for understanding geometry, drawings, physical systems, and design intent.
- Develop representation-learning methods that convert high-fidelity synthetic designs, CAD/CAE outputs, technical drawings, and agent trajectories into model-ready embeddings and training signals.
- Design and optimize training pipelines for mechanical design agents, including data generation, curriculum design, loss formulation, evaluation metrics, fine-tuning, and inference-time behavior.
- Analyze and improve subagent behavior across mechanical design workflows, including tool use, reasoning reliability, self-correction, planning, and collaboration with CAD/CAE execution systems.
- Collaborate closely with Research Engineers, CAD Engineers, and industry experts to raise the intelligence, generalization, reliability, and design quality of Mechanical AI’s agents.
What We’re Looking For
Mindset:
- Outlier builder with exceptional velocity.
- Exceptional resilience, ownership, initiative, and a problem-solving mindset, even under immense pressure.
- Willingness to work extended hours, including weekends, when necessary to meet critical deadlines.
- Outstanding teamwork in tackling interdisciplinary engineering challenges alongside experts from diverse backgrounds.
Must-have:
- Strong mathematical foundation in linear algebra, probability, optimization, and deep learning, with the ability to reason from first principles about neural network behavior.
- Demonstrable experience designing, training, debugging, and evaluating deep learning models, especially transformer-based, vision-based, or multimodal architectures.
- Ability to formulate complex mechanical design problems as learnable representations, training objectives, evaluation metrics, and inference-time behaviors.
- Exceptional learning and execution speed from concept to implementation.
- Proficiency in Python and hands-on experience with modern machine learning frameworks such as PyTorch or TensorFlow.
Nice-to-have:
- Experience with representation learning, self-supervised learning, reinforcement learning, post-training, or agent behavior optimization.
- Experience building training pipelines for large-scale models, including distributed training, experiment tracking, dataset curation, and model evaluation.
- Proficiency in LLM-based development environments, such as Claude Code or multi-agent development workflows.
- Familiarity with mechanical design, CAD, CAE, geometry processing, technical drawings, or physics-based simulation data.
- Expertise in numerical analysis, design optimization, computational mechanics, robotics, or scientific machine learning.
- Familiarity with batch processing using shell scripts in cloud environments, such as GCP or AWS.
CAD Engineer, Mechanical Intelligence
In this role, you will:
- Characterize mechanical designs across domains by identifying key parameters, geometric constraints, and formulation strategies for CAD automation.
- Prototype multiple design variations using advanced CAD software to explore parameterization, topology, manufacturability, and automation robustness.
- Collaborate with research engineers to build CAD templates for high-fidelity, manufacturing-grade synthetic designs with embedding-friendly representations for neural network training.
- Support research engineers in building and training state-of-the-art mechanical design agents that translate imagination into real-world designs by autonomously characterizing physical elements and governing principles.
What We’re Looking For
Mindset:
- Outlier builder with exceptional velocity.
- Exceptional resilience, ownership, initiative, and a problem-solving mindset, even under immense pressure.
- Willingness to work extended hours, including weekends, when necessary to meet critical deadlines.
- Outstanding teamwork in tackling interdisciplinary engineering challenges alongside experts from diverse backgrounds.
Must-have:
- First-principles critical thinking across diverse mechanical designs.
- Demonstrable achievements in complex industrial designs with cutting-edge performance.
- Exceptional learning and execution speed from concept to implementation.
- Exceptional proficiency in Siemens NX and willingness to learn additional CAD programs as needed.
- Ability to read technical English documentation with help from LLM tools when needed.
Nice-to-have:
- Deep domain expertise in tooling design (e.g., injection molding mold design, die casting die design, progressive stamping die design, etc.).
- Familiarity with additional CAD software (e.g., CATIA, AutoCAD, SolidWorks, or FreeCAD).
- Familiarity with Python.
- Familiarity with computer-aided engineering (CAE) software (e.g., Ansys, Abaqus, etc.).
5831 Forward Ave #251
Pittsburgh, PA 15217
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