ML Engineer

We're looking for a strong ML engineer who wants to work on problems that matter in the physical world. You'll build agentic AI pipelines, develop generative models, and create systems that reason about complex structured data. Our domain is engineering design geometry, physics, simulation and while deep domain expertise there is a plus, what we really need is someone with strong ML fundamentals who's excited to learn and apply them to hard, under-explored problems.

You'll have direct influence over the technical direction of the company and the product we ship. This is a ground-floor role with real ownership.

What You'll Do

Design and build agentic AI pipelines systems that can autonomously plan, execute, and adapt across multi-step workflows Develop generative models for structured, constraint-satisfying outputs (not just text and images think geometry, configurations, designs) Build surrogate models that learn to approximate expensive computational processes from data Create reasoning systems that can classify, manipulate, and make decisions over complex structured representations Integrate ML models into production pipelines for optimization and design space exploration Develop and maintain open-source tools that push the state of the art What We're Looking For Must-haves: Strong ML/AI fundamentals deep learning, transformers, generative models, reinforcement learning Experience building agentic systems or multi-step AI pipelines that plan, execute, and adapt Proficiency in Python and at least one ML framework (Py.

Torch, JAX, Tensor. Flow) Experience with structured prediction, 3D data, or scientific/engineering ML (not just NLP/vision) Comfort working in ambiguous problem spaces where the right approach isn't obvious Strong software engineering habits you write code that other people can read, test, and build on Strong pluses: Experience with 3D deep learning point clouds, implicit representations, graph neural networks Familiarity with computational geometry concepts meshes, BREP/NURBS, surface discretization Exposure to CAD/CAE workflows, simulation tools, or geometry kernels Background in surrogate modeling or physics-informed neural networks Experience with differentiable simulation or differentiable rendering Hands-on experience with CFD, FEM, or mesh generation Contributions to open-source scientific computing projects Aerospace, mechanical, or civil engineering domain knowledge Why Open.

Orion Real impact. Engineering simulation software is a bottleneck for innovation. We're removing it. Hard problems. Geometry + physics + AI is one of the most challenging and rewarding intersections in tech. Open source. We build in the open and contribute back to the community. Ground floor. You'll shape the product, the culture, and the technical direction from day one.

Benefits

Competitive salary with equity options Comprehensive Medical, Dental, and Vision insurance 401(k) retirement savings plan