We are looking for a Technical Project Manager who is a genuine bridge between our software and hardware teams — not just a coordinator, but someone who understands both worlds well enough to add value in each direction. You will translate field test results into engineering tasks, help the Lead Architect research and validate solutions, and champion AI tooling across the team.
Technical Background — Required
- 4+ years in software engineering; (CV/ML, technical lead, or architect role)
- Solid understanding of CV pipelines: detection, tracking, SLAM/visual odometry — enough to evaluate accuracy and latency trade-offs from field results.
- Python proficiency; C++ reading-level minimum.
- Hands-on experience with ONNX, TensorRT, or quantization workflows.
- Experience with deep learning frameworks (PyTorch or TensorFlow) and model optimization workflows.
- Working knowledge of ROS — nodes, topics, services, inter-module communication.
- Familiarity with edge platforms (NVIDIA Jetson or similar) and real-time constraints.
- Familiarity with autopilot ecosystems (ArduPilot, Betaflight) — enough to read architecture docs and contribute to technical decisions.
- Sensor fusion experience (Camera + IMU + GPS).
Project & Communication Skills
- Proven ability to manage complex technical projects across multi-disciplinary teams.
- Able to take polygon/field test results and convert them into clear, prioritized engineering tasks.
- Comfortable working directly with a Lead Architect — receiving high-level requirements, researching solutions, presenting structured proposals.
- Proficient with Jira and Confluence — task tracking, sprint management, structured documentation.
- Strong written and verbal communication; comfortable leading design reviews and justifying trade-offs.
AI Tools & Mindset
- Active daily user of AI tools (Claude, Cursor, Copilot, or similar).
- Genuine drive to become an internal AI champion — introduce tools, identify new use cases, raise the bar for the whole team.
Responsibilities
- Own end-to-end delivery across multiple workstreams (autonomous drone, turret control, and future platforms): planning, tracking, risk management, reporting.
- Primary technical liaison between software (CV/ML, ROS) and hardware/embedded teams — translating requirements, surfacing blockers, resolving misalignments.
- Attend polygon and field tests; translate results — including HIL/SIL outcomes — into structured engineering feedback and task descriptions.
- Work with the Lead Architect to research new approaches; prepare structured proposals for architectural decisions.
- Coordinate module integration (optical guidance, servo control, ROS layers) — interfaces, latency requirements, milestones.
- Maintain project documentation: requirements, test reports, decision logs, vendor deliverable tracking.
- Introduce and evangelize AI tooling across engineering workflows.
Nice to Have
- Hands-on experience with autonomous drones, FPV, or UAS platforms.
- ArduPilot or Betaflight ecosystem experience.
- MLOps or model deployment pipeline experience.
- Experience managing vendor/partner relationships — RFP processes, deliverable tracking.
- Systems thinker — holds the full picture while diving into details when needed.
- High ownership; comfortable with ambiguity and fast-moving field environments.
- Surfaces problems early with proposed solutions, not just issues.
Recruitment Process:
- CV Screening: Applications are reviewed within 24 hours.
- Pre-Screening: A short Q&A session (AI or with a recruiter) to assess your experience.
- Shortlisting: Selected candidates are presented to the hiring manager.
- Interview: Tech discussion with the project team.
- Offer & Onboarding: Successful candidates receive an offer and start the onboarding process.
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