Senior AI Research Scientist

Job not on LinkedIn

September 17

🌵 Arizona – Remote

Although this job is listed in Arizona, this company may be open to hiring remote in other states.

🏄 California – Remote

Although this job is listed in California, this company may be open to hiring remote in other states.

+27 more states

🌵 Arizona – Remote 🏄 California – Remote ⛰️ Colorado – Remote 🐊 Florida – Remote 🌺 Hawaii – Remote 🌽 Illinois – Remote 🌪️ Kansas – Remote 🦬 Montana – Remote 🎰 Nevada – Remote 🏖️ New Jersey – Remote 🌶️ New Mexico – Remote 🗽 New York – Remote 🌲 North Carolina – Remote 🌲 Oregon – Remote 🦀 Maryland – Remote 🍂 Massachusetts – Remote 🚗 Michigan – Remote ❄️ Minnesota – Remote 🏰 Missouri – Remote ⚓ Rhode Island – Remote 🎸 Tennessee – Remote 🤠 Texas – Remote ⛷️ Utah – Remote 🍁 Vermont – Remote ⚔️ Virginia – Remote ☕ Washington – Remote ⛰️ West Virginia – Remote 🧀 Wisconsin – Remote

⏰ Full Time

🟠 Senior

🧠 AI Research Scientist

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Slingshot Aerospace

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Aerospace • Artificial Intelligence • Security

Slingshot Aerospace is a leading AI and data-driven platform focused on optimizing satellite operations and enhancing space traffic coordination for operators worldwide. Renowned for its innovative solutions in space domain awareness, Slingshot Aerospace provides advanced tools for space security, defense missions, training, and market analysis. The company's comprehensive offerings include space object tracking, AI-driven training agents, and astrodynamics services. Through its Global Sensor Network and sophisticated data fusion techniques, Slingshot transforms disparate space data into actionable insights, improving operational efficiency and reducing risks in an increasingly complex orbital environment. Trusted by government and commercial space operators, Slingshot Aerospace is committed to making the space domain safe, sustainable, and secure for future generations.

51 - 200 employees

Founded 2020

🚀 Aerospace

🤖 Artificial Intelligence

🔐 Security

💰 $25.2M Grant on 2022-03

📋 Description

• Research and design AI systems, models, and advanced machine learning algorithms to augment physics-driven modeling and simulation systems • Enable intelligent AI agents to reason, plan, and adapt based on simulated and real-world sensor data • Lead innovation in AI-driven simulation tooling, identifying opportunities to enhance workflows through reinforcement learning, multi-agent systems, and hybrid modeling approaches • Collaborate with research, engineering, and product teams to build AI-driven solutions that meet mission-critical modeling and decision-support needs • Publish and present research outcomes at approved conferences and peer-reviewed journals • Contribute content to technical invention disclosures, including narrative, graphics, and engagements in support of patent development • Perform additional responsibilities (no more than 10% of duties) in support of the company’s data science and product development initiatives

🎯 Requirements

• Demonstrable experience in the application of sophisticated AI/ML methodologies to science and/or engineering disciplines, including deep learning, generative models (e.g. LLMs, diffusion models), agentic systems, reinforcement learning, computer vision, or other emerging areas of AI research • Hands-on experience developing and deploying supervised and/or unsupervised learning models • Software development experience • Familiarity with object-oriented or functional programming principles • Expertise in at least one 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 • Must be a U.S. citizen eligible for government clearances • (Preferred) Experience with fine-tuning LLMs, prompt engineering, retrieval-augmented generation (RAG), and domain adaptation for scientific/engineering datasets • (Preferred) Familiarity with Reinforcement Learning (RL) and multi-agent reinforcement learning • (Preferred) Strong understanding of neural networks, transformer architectures, attention mechanisms, and optimization methods • (Preferred) Experience building reusable internal tools (connectors, simulation frameworks, evaluation harnesses) • (Preferred) Peer-reviewed publications and/or presentations in a scientific discipline, AI/ML focus preferred • (Preferred) Demonstrable combined experience utilizing APIs, microservices, and workflows that merge physics simulation engines with AI training pipelines • (Preferred) Familiarity with common agentic protocols (MCP, A2A, etc.) • (Preferred) Practical working experience with physics simulations, Monte Carlo methods, probabilistic modeling, and Bayesian methods • (Preferred) 6+ years industry or commensurate academic research experience (e.g. PhD and/or MS) • (Preferred) Knowledge of parallelization, GPU acceleration, and performance optimization for simulations and training workloads • (Preferred) Experience with space and astrodynamics is valuable but not required • (Preferred) Demonstrated experience leading the development, implementation, and transition of technology to production is desirable