Director, ML/CV Engineering

Green Key Resources; Austin (TX); On-site; USD 190,000 - 230,000; Full time

Job summary

Green Key Resources seeks a Computer Vision/Machine Learning Engineering Manager to lead a real-time CV/ML stack for autonomous systems. You will be hands-on initially, growing into a player-coach role while partnering with senior leadership to shape the CV/ML roadmap and delivery.

You will own technical direction, manage and grow the team, and collaborate with hardware to deliver a field-ready platform in a fast-moving, engineering-driven environment.

Qualifications

• 15+ years of experience with ML-based CV and traditional image/signal processing, preferably in robotics/autonomous systems.
• Bachelor's degree in Computer Science, Electrical Engineering, or related field with ML/CV emphasis.
• Experience leading CV/ML engineers, with growth mindset toward broader management.
• Experience owning a technical roadmap for perception/autonomy products.
• Proficiency in Python and C++, with ML frameworks such as TensorFlow or PyTorch.
• Experience with sensors (cameras, LiDAR, RADAR) and embedded systems is strongly preferred.

Responsibilities

• Lead and grow a CV/ML team across levels, shaping careers and capabilities.
• Partner with senior leadership to define and execute CV/ML roadmaps.
• Translate product goals into quarterly team plans and staffing decisions.
• Contribute to real-time CV/ML algorithm development for detection, tracking and classification.
• Own delivery commitments for the CV/ML stack and coordinate with EE/Hardware teams.
• Drive quality via design reviews, testing, and validation across diverse environments.

Skills

Python

C++

Machine learning

Computer vision

Real-time processing

Robotics

Education

Bachelor's degree in Computer Science or Electrical Engineering

PhD in CV/ML or related field

Tools

TensorFlow

PyTorch

Embedded systems

Edge hardware (NVIDIA Jetson)

Job description

MUST BE ELIGIBLE FOR TOP SECURITY CLEARANCE

Company Overview

A cutting-edge defense technology startup is developing autonomous systems that combine advanced computer vision, machine learning, and precision control technologies to detect, track, and respond to emerging threats.

With an engineering-first culture, the company values technical excellence, innovation, and the development of technologies with real-world impact. The team is backed by experienced founders with a strong track record in robotics, software, and successful technology ventures.

About the Role

We are looking for a Computer Vision/Machine Learning Engineering Manager to join the Computer Vision and Machine Learning team developing autonomous systems, with a focus on real-time detection, tracking, and classification.

This begins as a player-coach role: you will contribute directly to the CV/ML stack while managing and growing the team, with your hands-on involvement shifting toward management, technical review, and team leadership as the organization scales.

You will partner with senior technical leadership to define the CV/ML technical roadmap and own its execution, translating product goals into team plans, priorities, and delivery commitments as the system progresses from prototype toward a highly reliable, field-ready platform.

What You’ll Do

• Manage and grow a CV/ML team: lead existing engineers, hire across levels from Junior through Senior Staff, and develop engineers' careers and technical capabilities.
• Partner with senior technical leadership to define the CV/ML technical roadmap and own its execution within your team.
• Translate product and company goals, including new system variants, expanded engagement ranges, and system-hardening milestones, into quarterly team plans, priorities, and staffing decisions.
• Contribute directly to the development and optimization of computer vision algorithms for real-time drone detection, tracking, and classification.
• Own delivery commitments for the CV/ML stack and coordinate integration milestones with electrical engineering and hardware teams.
• Drive engineering quality through design and code reviews, rigorous testing, and validation across a wide range of environmental conditions and operational scenarios.

What You’ll Need

• Deep expertise and 15+ years of experience working with machine-learning-based computer vision and traditional image and signal processing, ideally in robotics or autonomous systems, with a proven track record of deploying CV systems in real-time or safety-critical applications.
• At least a Bachelor's degree in Computer Science, Electrical Engineering, or a related field, with a strong focus on machine learning and computer vision.
• Experience managing or tech-leading a team of CV/ML engineers, along with a desire to grow into a broader management role.
• Demonstrated experience owning a technical roadmap or planning process for a perception, autonomy, or similar product area.
• Proficiency in Python and C++, with experience using machine learning frameworks such as TensorFlow, PyTorch, or similar technologies.
• Experience with embedded systems and sensor integration, including cameras, LiDAR, and RADAR, is strongly preferred.
• Comfort driving execution while collaborating with senior technical leadership on technical direction in a fast-moving, engineering-first environment.

You’ll Stand Out

• Formal management experience, including hiring and scaling a CV/ML or perception team at a startup or within a 0-to-1 product environment.
• Ph.D. in computer vision, machine learning, or a related field.
• Experience with object detection and tracking in challenging real-world conditions, including small targets, cluttered backgrounds, or low-contrast imagery.
• Background in defense, aerospace, robotics, or autonomous systems where performance and reliability requirements are critical.
• Experience deploying and optimizing models on edge hardware such as NVIDIA Jetson or similar embedded GPU platforms.
• Experience with multi-sensor fusion across modalities, including optical and infrared cameras.
• Experience leading teams through field testing and hardware integration cycles, rather than software-only development.