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’ll Be 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 in relevant research and development (R&D) of AI systems, models, and advanced machine learning algorithms that augment 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) US Security Clearances
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 neural networks, transformer architectures, attention mechanisms, and optimization methods to extend or fine-tune transformer-based models
Experience building reusable internal tools (connectors, simulation frameworks, evaluation harnesses) to refine simulation-based datasets for AI agent training in support of research, data-driven insights/analytics, and model development
Hands-on experience supporting the development and deployment of supervised and/or unsupervised learning models
One or more peer reviewed articles, conference papers, and/or presentations in a science or engineering discipline
Demonstrable combined experience indicative of skillsets required to utilize APIs, microservices, and workflows that merge physics simulation engines with AI training pipelines
Familiarity with common agentic protocols (MCP, A2A, etc...)
Practical working experience with physics-based simulation, and statistical methods (e.g. monte Carlo methods, probabilistic modeling, and Bayesian methods)
Working knowledge of parallel computing, GPU acceleration, and performance optimization for simulations and training workloads.
Experience with space and astrodynamics is valuable but not required
We're building a constellation here, not looking for identical satellites. Every member of the team brings different capabilities to the same mission. If your orbit intersects with ours and you're mission-ready, send it.
Location: Remote, US
Salary Range: $120,000 - $170,000 + Equity and Benefits
Classification: Full-time Exempt (computer professional exemption)
US-based Candidates: we are currently only able to hire residents of the following U.S. states: AL, AZ, CA, CO, DC, FL, GA, HI, IL, IN, KS, MA, MD, MI, MN, MO, MT, NC, NJ, NM, NV, NY, OH, OK, OR, RI, TN, TX, UT, VA, WA, WI, WV We are unable to consider candidates residing in other U.S. states at this time.
Internationally-based Candidates: we are currently only able to hire residents of the following locations: United Kingdom. We are unable to consider candidates residing in other countries at this time.
Equity, Diversity & Inclusion are key to our success. We are an Equal Opportunity Employer and our employees are people with different strengths, experiences, and backgrounds, who share a passion for creating a safer, more connected world. Diversity not only includes race and gender identity, but also national origin, citizenship, sex, color, veteran status, disability, genetic information, or any other protected characteristic that is part of one’s identity. All of our employees’ points of view are key to our success, and we embrace individuality.
Skills Required
- Active United States security clearance at Secret level minimum; Top Secret preferred
- Master’s degree in a related field and at least two years of experience in a similar role, or a PhD with relevant research projects
- Demonstrable experience applying AI and machine learning methodologies, including deep learning, generative models, agentic systems, reinforcement learning, or computer vision
- Familiarity with object-oriented programming and functional programming principles
- Expertise in at least one modern high-level programming language, such as Python, R, C++, or Java
- Experience with collaborative source code management and maintenance processes, including GitHub, code reviews, or CI/CD
- Ability to work within multidisciplinary teams across military, government, and industry partners
- Excellent verbal and written communication skills
- Passion for space and AI or machine learning applications
- Experience fine-tuning LLMs, prompt engineering, retrieval-augmented generation, or domain adaptation for scientific and engineering datasets
- Familiarity with reinforcement learning and multi-agent reinforcement learning
- Understanding of neural networks, transformer architectures, attention mechanisms, and optimization methods
- Experience building reusable tools, simulation frameworks, evaluation harnesses, or AI agent training datasets
- Experience developing and deploying supervised or unsupervised learning models
- Peer-reviewed articles, conference papers, or presentations in a science or engineering discipline
- Experience using APIs, microservices, and workflows integrating physics simulation engines with AI training pipelines
- Familiarity with agentic protocols such as MCP or A2A
- Experience with physics-based simulation and statistical methods such as Monte Carlo, probabilistic modeling, or Bayesian methods
- Working knowledge of parallel computing, GPU acceleration, and performance optimization
- Experience with space and astrodynamics
What We Do
Slingshot Aerospace develops Space Operations Intelligence and Autonomy solutions for defense, commercial, and sovereign space operators. Its integrated platform combines sensor data, data fusion, simulation, AI-driven analytics, and mission workflows to create a real-time operational picture of the space domain. The company supports space-object awareness, maneuver planning, anomaly response, mission coordination, training, and increasingly autonomous execution, helping customers protect assets and operate with greater confidence.








