Engineering Manager, Perception and Machine Learning
Havoc AI
- Location
- Remote
- Employment
- Full Time
- Work model
- Remote
- Level
- Senior
- Posted
- 1h ago
Skills
About this role
About Us
Havoc is a leader in all-domain collaborative autonomy. Its software-defined hardware approach powers military and commercial-grade autonomous systems across sea, air, and land to sense, decide, and act together in complex and contested environments. Havoc connects assets, enabling them to share information, adapt in real time, and continue operating even when communications are disrupted or denied. Havoc optimizes mission performance and minimizes human risk. Havoc was founded in 2024 and headquartered in Providence, Rhode Island. Learn more at Havoc: All-Domain Collaborative Autonomy .
About the Role
As the Engineering Manager, Perception & Machine Learning, you will lead the team responsible for developing the perception and machine learning capabilities that enable HavocAI’s autonomous systems to understand and operate within the world around them. This is a people leadership and technical management role for someone with a strong foundation in perception, machine learning, computer vision, sensor fusion, or robotics. You will be responsible for building and developing the team, setting priorities, driving execution, and ensuring perception capabilities move effectively from research and development into reliable, fielded products. You’ll work closely with Autonomy, Embedded, Data, Simulation, Product, Programs, and Field Operations to translate mission and product requirements into clear engineering priorities. You should be comfortable balancing technical decisions, team development, execution, and changing priorities in a fast-moving environment.
Key Responsibilities
Lead, manage, and develop a team of perception, machine learning, sensor fusion, and software engineers. Own team planning, prioritization, execution, and delivery against the Perception & ML roadmap. Establish clear goals, responsibilities, and expectations for the team and hold engineers accountable for results. Partner with senior technical contributors to guide architecture and technical decisions across computer vision, sensor fusion, tracking, detection, classification, segmentation, and related capabilities. Oversee development and maturation of model training, evaluation, data curation, validation, and deployment workflows. Ensure perception capabilities are designed around real-world performance, reliability, compute constraints, and field readiness. Partner cross-functionally with Autonomy, Embedded, Data, Simulation, Hardware, Product, Programs, and Field Operations. Translate customer, mission, and product requirements into clear engineering priorities and execution plans. Use field data, testing results, and operator feedback to prioritize improvements and address performance gaps. Establish effective engineering practices around design reviews, code reviews, testing, model evaluation, release readiness, and technical documentation. Identify execution risks, dependencies, resource constraints, and technical tradeoffs and communicate them clearly to engineering leadership. Coach and develop engineers through regular feedback, performance management, career development, and mentorship. Recruit, interview, onboard, and retain high-performing engineers as the team grows. Foster a culture of technical excellence, ownership, collaboration, and continuous improvement.
Qualifications
Bachelor’s degree in Computer Science, Machine Learning, Robotics, Electrical Engineering, Computer Engineering, or a related technical field. 7+ years of relevant engineering experience in perception, machine learning, computer vision, robotics, autonomy, sensor fusion, or related fields. 2+ years of direct people-management experience, ideally managing engineers in a highly technical environment. Strong technical understanding of modern perception systems, including computer vision, sensor fusion, tracking, detection, classification, or ML-based scene understanding. Experience taking ML or perception capabilities from development through testing and