AI Research Scientist, Applied AI

Meet Slingshot

At Slingshot Aerospace, we're on a mission to make space safer and more secure for everyone. Our work directly impacts global security, disaster response, climate monitoring, and the critical infrastructure that connects our world. We're a team of builders, thinkers, and problem-solvers who believe that the next generation of space operations will be powered by better data and smarter software.

We move fast, we're not afraid to fail, and we believe the best ideas can come from anywhere—whether you're in engineering, sales, product, or operations. If you want to work on something that truly matters, with people who care deeply about the impact we're making and help shape the future of an industry that's just getting started, you're in the right place.

Meet Slingshot

At Slingshot Aerospace, we’re on a mission to make space safer and more secure for everyone. Our work directly impacts global security, disaster response, climate monitoring, and the critical infrastructure that connects our world. We’re a team of builders, thinkers, and problem-solvers who believe that the next generation of space operations will be powered by better data and smarter software.

What You’llBe Launching

As an AI Research Scientist, you will join the AI and Innovation department within Slingshot’s Technology organization. You will contribute directly to Slingshot’s vision to accelerate space sustainability and create a safer, more connected world. You will participate in the identification, development, and integration of novel algorithms and models, leveraging diverse data streams and advanced intelligence engines, and the subsequent integration of those technologies into prototypes and broader AI systems across the Slingshot platform.

Your Mission (Should you choose to accept it)

• Engage inrelevantresearch and development(R&D) ofAI systems, models,and advanced machine learning algorithmsthataugment physics-driven modeling and simulation systems

• Explore and implement AI-powered simulation tooling in support of AI workflows through reinforcement learning, multi-agent systems, and hybrid modeling approaches

• Collaborate with research, engineering, and product teams to build AI-powered solutions that meet mission-critical modeling and decision-support needs

• Engage in and support the drafting and review of conference and journal articles and presentations, sharing advances with both internal stakeholders and the wider research community.

• Contribute content to technical invention disclosures, including associated narrative, graphics, and engagements in support of patent development

• Perform additional responsibilities (no more than 10% of duties) in support of the company’s technology and product development initiatives

Pre-flight Checklist

• Must have an Active US Security Clearance (Secret Minimum, Top Secret Preferred)

• Masters in related field + Minimum of 2 years' experience in similar role (or PhD with relevant research projects)

• AI/ML expertise

  • Demonstrable experience in the application of AI/ML methodologies including, but not limited to, deep learning, generative models (e.g. LLMs, diffusion models), agentic systems, reinforcement learning, computer vision, or other emerging areas of AI research

• Software development experience

  • Familiarity with object-oriented paradigms and functional programming principles

  • Expertise in at least one modern high-level programming language (e.g. Python, R, C++, Java)

  • Collaborative source code management and maintenance processes (e.g. Github, code reviews, CI/CD)

• Ability to work within multi-disciplinary teams in a fast-paced, evolving operational environment that spans military, government, and industry partners

• Excellent verbal and written communication skills

• Passion for Space and AI/ML applications

Bonus Cargo

• Experience with fine-tuning LLMs, prompt engineering, retrieval-augmented generation (RAG), and domain adaptation for scientific/engineering datasets using modern ML frameworks and model hubs

• Familiarity with Reinforcement Learning (RL) and multi-agent reinforcement learning to enable training agents that learn strategies in simulation and real-world contexts

• Practical understanding of…