Senior AI Engineer; TS​/SCI

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.

Role is based in Colorado Springs, CO, and Slingshot will provide relocation assistance!

What You’ll Be Launching

As a Senior AI Engineer focused on Agentic Evaluation and Verification and Validation (V&V), you will join the AI and Data Science team within Slingshot’s Research and Development organization. You will contribute to advancing how intelligent systems are evaluated and validated for mission-critical space operations.

This role focuses on building and scaling evaluation frameworks, benchmarks, and simulation-backed validation systems for agentic AI systems, including multi-step, tool-using, and autonomous decision-making workflows powered by LLMs and reinforcement learning. Your work will directly support the development of reliable and trustworthy autonomous mission planning systems.

You will partner closely with AI researchers and domain experts to translate real-world mission concepts into structured, testable evaluation systems.

Your Mission (Should you choose to accept it)

• Extend and maintain Slingshot’s V&V SDK and evaluation framework for simulation-backed validation of agentic AI systems

• Design and implement agent-level and end-to-end evaluations, including benchmark scenarios, scoring logic, and experiment harnesses

• Build benchmark scenarios and tooling that measure planning, reasoning, and operational performance for autonomous mission planning systems

• Translate astrodynamics and mission-domain concepts into executable evaluation scenarios and simulation configurations

• Develop reusable SDK interfaces, adapters, and evaluation utilities that connect V&V systems, TALOS benchmarks, and agent workflows

• Define and apply metrics for capability evaluation, failure analysis, regression detection, and comparative benchmarking

• Partner with cross-functional teams to identify evaluation needs and contribute to improving coverage of critical capabilities

• Contribute to best practices for evaluating complex, autonomous AI systems

• Uphold strong engineering standards through testing, documentation, reproducibility, and maintainable system design

Pre-flight Checklist

• 6+ years of experience in software engineering, machine learning engineering, applied AI, or equivalent experience

• Strong Python engineering skills with experience building SDKs, libraries, or evaluation tooling

• Experience designing evaluation frameworks, benchmarks, metrics, or test harnesses for AI/ML systems

• Ability to analyze system behavior, identify failure modes, and evaluate performance in complex autonomous or semi-autonomous systems

• Familiarity with modern agent frameworks, orchestration patterns, or protocol-based integrations

• Experience working in cross-functional, multidisciplinary teams

• Strong written and verbal communication skills

• Bachelor’s degree in a relevant science or engineering field, or equivalent experience

• Must be a U.S. citizen and possess an active Top Secret / SCI (TS/SCI) clearance.

Bonus Cargo

• Experience in autonomous systems such as self-driving or ADAS, including perception, planning, simulation, or safety validation

• Experience developing or evaluating agentic AI systems, including multi-step, tool-using, or autonomous workflows (e.g., LLM-based agents, planning agents, or reinforcement learning approaches)

• Experience with reinforcement learning systems and simulation-based evaluation

• Familiarity with benchmark design, experiment tracking, and trace-based…