ML/AI/CS Intern

Oligo is building a manufacturing-in-the-loop foundation model to automate spacecraft design and production worldwide. Our approach allows customers to focus entirely on their own technology and mission objectives, while we handle everything, from design and manufacturing to launch and operations. Leveraging cutting edge AI-driven generative design and automated manufacturing, our ex-MIT, Harvard, and NASA JPL team work to create the most advanced payload-specific spacecraft at scale in weeks over months.
With world‑class advisors on our board, and fresh funding from top investors like Lux Capital, we’re always on the lookout for exceptional builders, fast learners, and ambitious engineers. Whether your passion lies in spacecraft systems, avionics, ML/AI, or advanced manufacturing, you’ll be collaborating across disciplines on real missions that fly, perform in orbit, and scale internationally.
We pair world-class AI/ML talent with top-tier satellite engineers under one roof to reimagine how space systems are built, starting from first principles. No bureaucracy. No legacy thinking.
If you think you’re a fit, we are extremely excited to meet you.

Oligo builds vertically integrated infrastructure for automated spacecraft design and manufacturing. Our AI software stack, Zenith, turns raw mission requirements into flight-ready spacecraft through a pipeline of agentic AI systems, embedded simulation, and hardware-in-the-loop validation.

We’re hiring interns with a background in ML/AI and systems engineering to contribute to core algorithms across the stack: from geometry-aware generative design to system-level reasoning agents. You’ll work alongside spacecraft engineers and flight software developers to build AI that doesn’t just simulate spacecraft—it designs them.

This is a hands-on role for students or early-career engineers who want to apply cutting-edge AI to real-world hardware. You’ll gain experience in spacecraft engineering, astrodynamics, and manufacturability by building models that directly influence how our satellites are designed, built, and flown.

Depending on project alignment and your background, you may work on:

• Systems AI (Architecture & Requirements)

  • Build and fine-tune LLMs/VLMs that parse technical documents (RFPs, datasheets, ICDs, requirements trees) into subsystem constraints and architectures.

  • Develop models that detect contradictions, inconsistencies, or gaps in requirements and suggest valid configurations.

  • Contribute to automated generation of system artifacts: interface tables, block diagrams, FMEAs, and verification specs.

  • Help formalize spacecraft system architecture knowledge (e.g., JSON schemas, graph-based representations) for model supervision and traceability.

• Generative Design AI (Physics & Geometry)

  • Design and train reinforcement learning agents to explore multi-variable design spaces (structural, thermal, orbital, manufacturability).

  • Develop CNN-based topology optimizers (e.g., U-Net + FEniCSx) to reduce mass in structural components while maintaining stiffness.

  • Interface with CAD kernels (OpenCascade, CadQuery) and FEA/simulation frameworks (Ansys, GMAT, Thermal Desktop).

  • Construct multi-agent AI systems that iteratively redesign spacecraft configurations using embedded physics models.

• Cross-cutting Work

  • Integrate your models into Zenith’s pipelines, ensuring outputs flow downstream into design, simulation, and manufacturing agents.

  • Help build a multimodal training dataset (text, diagrams, configs, simulation outputs) from real spacecraft programs.

  • Collaborate daily with spacecraft engineers, systems architects, and test leads to ensure AI outputs match real-world engineering practices.

  • Optionally contribute to customer-facing demos or onboarding automation for international engagements.

Minimum Qualifications

• Currently pursuing or recently completed a degree in Computer Science, ML/AI, Engineering, or related technical field.

• Hands-on experience with ML/AI through research, projects, or internships.

• Strong proficiency in Python and familiarity with ML frameworks (PyTorch, JAX, or TensorFlow).

• Interest in at least one of:

  • Vision-language models and automated document parsing

  • Reinforcement learning for constrained optimization/control

  • Simulation-aware ML, surrogate modeling, or physics-informed pipelines

  • Geometry/topology optimization with CNNs or CAD/FEA integration

Preferred Skills & Experience

• Exposure to structured engineering artifacts (ICDs, requirements tables, CAD trees, verification matrices).

• Familiarity with spacecraft concepts, astrodynamics, or control systems.

• Background in MBSE or architecture design tools (Capella, CORE, or custom frameworks).

• Hands-on ability to prototype or debug hardware/software systems.

• Clear communication skills and ability to work across AI + engineering disciplines.