Senior Manager, Engineering - Hivemind Reasoning (R6291)

Shield; San Mateo (CA); On-site; USD 180,000 - 240,000; Full time

Benefits offered by this job

Equity

Bonus

Benefits

Job summary

Shield AI seeks a senior leader to build and lead AI/ML teams developing reasoning models for unmanned platforms across air, sea, land, and space. You will shape strategy, coordinate with perception, MLOps, and simulation teams, and drive edge-enabled capabilities in contested environments.

You will recruit and mentor engineers, define training pipelines, and ensure robust evaluation and deployment of models with real-world data. Excellent leadership and clear communication are essential.

Qualifications

• 10+ years in ML model development and production deployment.
• 5+ years of people management leading engineering or research teams.
• Deep expertise in foundation models and modern DL techniques.
• Experience with edge computing and real-world constrained environments.

Responsibilities

• Define and lead research programs for single-agent and multi-agent AI for unmanned platforms.
• Build and guide a world-class team of AI/ML engineers and researchers.
• Design training pipelines, evaluation criteria, and data strategies.
• Coordinate with perception, MLOps, simulation, and inference teams.
• Translate edge constraints into model and training decisions in collaboration with hardware teams.
• Communicate tradeoffs and model limitations to leadership and partners.

Skills

Leadership

Strategic thinking

Communication

Mentoring

Decision making

Education

BS/MS in Computer Science or Engineering

PhD (preferred)

Tools

PyTorch

JAX

TensorFlow

Edge AI hardware (NVIDIA Jetson)

Job description

Shield AI is a venture-backed defense-tech company with the mission of protecting service members and civilians with intelligent systems. Its products include Hivemind autonomy software, V-BAT and X-BATaircraft, and Aechelon simulation and synthetic reality technologies. With offices and facilities across the U.S., Europe, the Middle East, and Asia-Pacific, Shield AI’s technology actively supports operations worldwide. For more information, visit www.shield.ai. Follow Shield AI on LinkedIn, X, Instagram, and YouTube.

Job Description:

Lead the future of physical AI in defense by building the reasoning models for unmanned autonomous systems, across aerial, maritime, land, and space domains.

We are looking for a leader with not only the deep technical expertise or the abilities to build a world-class team, but also someone with the strong conviction that AI will protect service members and civilians. This role is responsible for building and leading a team that researches and designs models, establishes training pipelines and rigorous evaluations, and ensures mission effectiveness at the edge under real-world constraints. These models must support individual vehicles and coordinated teams of hundreds of agents, while meeting the constraints of operating at the edge in contested environments.

In this role, you’ll partner closely withworld-class teams in perception, MLOps, large-scale simulation, and inference to deliver capabilities as part of the broader Hivemind Enterprise team.

What success looks like: your team’s reasoning models deliver exceptional outcomes for baseline platforms and missions, which can then be rapidly and confidently adapted to new platforms and missions by our solutions engineering organisation.

What you’ll do:

• Build & lead the team that designs, trains, adapts, and evaluates reasoning and cognition models intended for deployment across our unmanned platforms.

• Define the technical roadmap for single-agent and multi-agent models, including architecture choices, training methods, data strategy, and evaluation criteria.

• Make informed tradeoffs among foundation-model approaches (e.g. VLMs, VLAs), specialized deep-learning models, and traditional AI/ML methods.

• Advance model capabilities for interpreting context, reasoning over time, coordinating among agents, and adapting to incomplete or changing information.

• Drive training efforts, including appropriate use of self-supervised learning, imitation learning, reinforcement learning, and distillation.

• Establish rigorous evaluations in simulation and with real-world data. Measure generalization, robustness, uncertainty, and performance as team size and operating conditions change.

• Recruit, develop, and manage a high-performing team of AI/ML engineers.

• Translate edge constraints into model-design and training decisions in partnership with hardware, autonomy, and inference teams.

• Communicate technical tradeoffs and model limitations clearly to research, engineering, product, and organizational leadership.

• Deliver trained models and clear performance evidence to partner teams.

• Create tight feedback loops among model failures, data curation, training experiments, and re-evaluation.

Required qualifications:

• BS/MS in Computer Science, Electrical Engineering, Mechanical Engineering, Aerospace Engineering, and/or similar degree, or equivalent practical experience. Typically requires a minimum of 10 years of related experience with a Bachelor’s degree; or 9 years and a Master’s degree; or 8 years with a PhD; or equivalent work experience.

• Exceptional track record of leading the development and deployment of state‑of‑the‑art ML models in production environments, making difficult prioritisation decisions, and communicating tradeoffs across disciplines.

• 5+ years of people management experience leading engineering or applied research teams, with demonstrated success hiring, coaching, developing technical leaders, and managing performance.

• Deep expertise in foundation models, modern deep learning, model architectures, and training ecosystem.

• Strong command of training and adaptation approaches such as self‑supervised learning, supervised fine‑tuning, imitation learning, reinforcement learning, and distillation.

• Experience defining datasets, training objectives, experimental workflows, benchmarks and reproducible criteria for comparing models.

• Fluency in traditional AI/ML techniques and an ability to combine them thoughtfully with learned models.

• Experience with training frameworks such as PyTorch, JAX, TensorFlow, etc.

• Experience with multimodal, embodied, robotic, or autonomous‑system models; familiarity with VLA models and world models is particularly relevant.

• Strong familiarity with traditional robotics algorithms, e.g. path planning, probabilistic modeling, optimization approaches, etc.

• Familiarity with NVIDIA Jetson and other edge compute platforms (e.g. Qualcomm, AMD, etc.)

Preferred qualifications:

• PhD in Computer Science, Electrical Engineering, or similar degree.

• Expertise in optimising and deploying ML models to resource‑constrained hardware platforms.

• Experience with simulation‑to‑real on autonomous systems.

• Experience managing managers or leading multiple engineering teams.

• Experience with unmanned autonomous systems in defense applications

• Familiarity with large, distributed systems

• Familiarity with high‑fidelity simulation, e.g. rigid body dynamics, sensor & actuator models, etc.

• Experience managing and interfacing with external partners and vendors

#LI-DM2

#LE

Full-time regular employee offer package:

Pay within range listed + Bonus + Benefits + Equity

Temporary employee offer package:

Pay within range listed above + temporary benefits package (applicable after 60 days of employment)

Salary compensation is influenced by a wide array of factors including but not limited to skill set, level of experience, licenses and certifications, and specific work location. All offers are contingent on a cleared background and possible reference check. Military fellows and part‑time employees are not eligible for benefits. Please speak to your talent acquisition representative for more information.

Shield AI is proud to be an equal opportunity workplace and is an affirmative action employer. We are committed toequal employment opportunity regardless of race, color, ancestry, religion, sex, national origin, sexual orientation, age, marital status, disability, gender identity or Veteran status. If you have a disability or special need that requires accommodation, please let us know.