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.
Role Overview
Oligo builds vertically integrated infrastructure for automated spacecraft design and manufacturing. Our AI software stack, Zenith, turns raw mission requirements into flight-ready spacecraft using a pipeline of agentic AI systems, embedded simulation, and hardware-in-the-loop validation.
We’re hiring a Senior Software/AI Engineer with a background in both ML/AI as well as classical automation to lead the advancement of core algorithms used for generative design, simulation-aware geometry creation, process acceleration, and algorithmic tooling for spacecraft engineers. You’ll work alongside spacecraft engineers and flight software developers to build automations that don’t just simulate reality—but design it.
This is a hands-on, high-leverage role for mid to late-career engineers interested in applying cutting-edge algorithms to real-world hardware. You’ll learn spacecraft engineering, astrodynamics, and manufacturability by building models that directly influence how our satellites fly.
What You’ll Lead
Develop and maintain foundation models with strong spatial reasoning capability that act as the basis for various spacecraft design automations.
Design complex self-supervised and low-data/low-fidelity model training schemes using the limited real-world spacecraft data.
Develop and deploy automation tooling to accelerate spacecraft design processes using classical and learning-based methods.
Develop and deploy agentic VLM frameworks that parse technical documents, datasheets, and system specs into structured engineering constraints.
Build and train models using reinforcement learning to explore high-dimensional multivariable design spaces—balancing structural, thermal, orbital, and manufacturability objectives.
Interface with physical simulation tools (Ansys, GMAT, Thermal Desktop) and CAD environments (OpenCascade, CadQuery, SolidWorks plugins) to ground designs in physical constraints.
Collaborate daily with engineers building the real hardware—what you code will be tested in thermal chambers, vibration tables, and flown on orbit.
What You’ll Bring
Minimum Qualifications
Master’s degree or higher in Computer Science, ML/AI, or a related technical field.
Significant experience in ML/AI through academic or industry research, personal projects, or internships beyond coursework.
Significant experience in classical algorithm development through academic or industry research, personal projects, or internships beyond coursework.
4+ years of experience building advanced ML/AI systems (LLM agent frameworks, complex RL algorithms, transformer architectures (text, vision, diffusion, etc.), data and compute efficient architectures).
Strong proficiency in Python (specifically PyTorch and other standard Python libraries), C++, and C#. Proficiency or willingness to learn modern tooling.
Ability to think clearly about tradeoffs between simulation fidelity, inference speed, and manufacturability.
Preferred Skills and Experience
Experience working with physics-informed deep learning models and PDE-grounded neural networks.
Experience working with and adapting enterprise software (e.g. Ansys, SolidWorks, Fusion360, etc.) outside of API usage (e.g. OS calls, plugins, editing binaries, etc.).
Hands-on ability to prototype, build, and debug hardware systems—bonus if you’ve worked with microcontrollers, sensors, or test rigs.
Prior experience working in the aerospace industry. Familiarity with spacecraft concepts, astrodynamics, or control systems.
Familiarity through coursework or beyond with data/differential privacy methods for deep learning.
Pay Range
Salary range: $140,000 - $164,000 per year.
This role is on-site in Hawthorne, CA.
Benefits
Equity
Unlimited PTO
Medical (Platinum coverage), Vision, & Dental Insurance
Catering provided on-site everyday.
Additional Information
You may be eligible for our suite of benefits including medical, vision & dental coverage.