AI Space Systems Software Engineer
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 Software Engineer with a background in ML/AI and systems engineering to help advance core algorithms used for generative design, simulation-aware geometry creation, and multi-domain system optimization. You’ll work alongside spacecraft engineers and flight software developers to build AI that doesn't just simulate reality, but designs it.
We’re hiring specifically for an early-career AI Space Systems Software Engineer to help us build the next layer of intelligence in our design stack: intelligent agents that reason across spacecraft requirements, mission architectures, and system constraints. You’ll contribute to the development of large-scale language and vision-language models trained on real systems engineering data, creating tools that accelerate and validate satellite design by automating model based systems engineering for the first time.
This is a hands-on, high-leverage role for early-career engineers interested in applying cutting-edge AI to real-world hardware. You’ll learn spacecraft engineering, astrodynamics, and manufacturability by building models that directly influence how our satellites fly and you’ll be a part of the manufacturing process to bring it to life.
What You'll Do
Construct multi-agent AI systems that simulate, evaluate, decompose, and iteratively redesign spacecraft configurations using embedded physics models.
Contribute to our infrastructure for automated, high-context model training using flight data, past simulations, and test results-enabling real-time design intelligence.
Build and fine-tune LLMs and VLMs capable of parsing technical documents (RFPs, datasheets, ICDs, requirements trees) into formal subsystem constraints and architectural schemas.
Develop models that detect contradictions, inconsistencies, or gaps in requirement sets and suggest valid configurations or mitigations based on historical precedent.
Help construct a multimodal training dataset by collecting, aligning, and annotating text, diagrams, system specs, and config trees from past and current spacecraft programs.
Design a formal representation of system architecture knowledge (e.g., JSON schemas, graph-based topologies) for model supervision and traceability.
Contribute to automated generation of interface tables, block diagrams, FMEAs, and verification specs from unstructured or customer-supplied inputs.
Integrate your models into Zenith’s payload ingestion and system decomposition pipelines, ensuring outputs flow into downstream design, simulation, and manufacturability agents.
Collaborate with spacecraft engineers, systems architects, and test leads to ensure model outputs match real-world engineering practices and testable subsystem boundaries.
Optionally support go-to-market initiatives including demo preparation, customer onboarding automation, and BD tooling for international engagements.
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
Bachelor’s/Masters degree (or final semester) in Computer Science, ML/AI, Engineering, or a related technical field (or willing to leave existing program)
Experience in ML/AI through research, personal projects, or internships (not just coursework).
2+ years of experience building advanced ML systems (deep learning, RL, planning, or LLM agent frameworks).
Strong proficiency in Python, including PyTorch, JAX, or TensorFlow, and modern tooling (e.g., LangChain, Ray, FastAPI, DVC, Docker).
Experience working on one or more of the following:
Vision-language models (e.g., transformers, CLIP, Flamingo-like systems)
Reinforcement learning for constrained optimization or control
Simulation-aware ML pipelines, physics-informed ML, or surrogate modeling
Automated document parsing, retrieval-augmented generation (RAG), or knowledge graph-based systems.
Clear written and verbal communication skills and the ability to collaborate across both software and spacecraft teams.
Preferred Skills and Experience
Hands-on ability to prototype, build, and debug hardware systems-bonus if you’ve worked with microcontrollers, sensors, or test rigs.
Willingness to work extended hours or weekends when necessary to meet mission-critical deadlines.
Familiarity with spacecraft concepts, astrodynamics, or control systems is helpful but not required
Experience working with structured engineering artifacts (ICDs, requirements tables, CAD trees, verification matrices).
Background in MBSE or spacecraft architecture design tools (e.g., Capella, CORE, or internal tooling).
Interest in aerospace design processes including thermal, structural, or power subsystem allocation.
Experience with project teams, technical clubs, or cross-disciplinary engineering efforts focused on space or robotics.
A passion for building AI that engineers can trust-legible, auditable, and grounded in real flight programs.
Pay Range
Salary range: $110,000 - $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.