Applied ML / After-Action-Review (AAR) Engineer

We are seeking a highly skilled Applied ML / After-Action-Review (AAR) Engineer to join a defense technology startup based in Boston, Massachusetts.

About the Company

Our client is a defense technology startup building next-generation training systems powered by AI and immersive technologies. The company develops solutions at the intersection of artificial intelligence, computer vision, XR, reinforcement learning, and embedded systems to support military training.

In this role, you will work on:

• Scoring and grading from Unity simulation telemetry against user-defined CQB metrics (target discrimination, corner slicing, room-entry technique, use of cover, stance, and others).

• Stance and posture metrics from inferred body pose (headset + rifle to full body; sparse-tracker pose inference).

• An LLM-based AAR copilot: natural-language debriefs, coaching feedback, and a queryable post-engagement review grounded in doctrine / TTPs.

Required Qualifications:

• 2-5+ years of relevant professional experience

• Applied ML experience with strong skills in Python / PyTorch.

• Experience in LLM application engineering (prompting, retrieval / RAG, evaluation).

• Turning structured telemetry into reliable, well-validated metrics.

• Strong plus:

  • Multimodal modeling (combining telemetry with other signals).

  • Experience building grading, analytics, or coaching-style products.

• Ability to work independently and thrive in a fast-paced startup environment.

• Excellent communication skills in English.

Preferred Qualifications: Complex games, training analytics, or defense background.

Team & Culture

• Approximately 15 people across full-time employees, contractors, and advisors

• Flat organizational structure with significant ownership and autonomy

• Early hires have opportunities to become founding-level contributors and future leaders

• Culture centered around the philosophy: "Work with us, not for us."

• We're looking for engineers who are:

  • Driven and resourceful.

  • Excited by startup environments.

  • Comfortable with ambiguity and rapid iteration.

  • Motivated by ownership and long-term impact.

  • Interested in equity participation and building something meaningful from the ground up.

Given the nature of the work, candidates should be supportive of the company's defense mission.

Recruitment Process

1. Prescreen call with the Recruitment Manager.

2. Prescreen with the client.

3. Technical interview with the client.

Candidates are encouraged to share a GitHub profile, portfolio, or examples of relevant work.
Hiring decisions are often made shortly after the interview process.