Senior Engineering Manager (Hivemind Reasoning) Shield AI
AI aircraft pilots for service member and civilian protection.
Permanent
San Mateo, CA
Remote: Not specified
290K to 430K $ a year
> 5 years
Skills we're looking for
AI and machine learning
Robotics
TensorFlow
Foundation
Pytorch
You'll be part of
1156 employees
Intelligence artificielle / Machine Learning
Our take
Self-driving vehicles present enormous benefits for the transportation industry, with the potential to increase the safety and efficiency of cars, trucks, and commercial airlines. However, such technological innovation is less present within the military transportation sector, despite the benefits that AI piloting would produce for the operation of drones and aircraft. Shield AI is developing Hivemind, an AI-driven pilot that can operate drones and aircraft autonomously, without needing GPS, communications, or pilots. In addition to navigation, the technology is also designed to facilitate indoor building clearance, integrated defense breaching, and has the ability to learn and autonomously execute aircraft missions. Whilst similar AI-driven pilot software has begun to appear in Chinese military research programs, Shield AI's target is the US. By developing fully autonomous piloting integrations for any type of aircraft, the company aims to future-proof the US military without the need for bespoke aircraft design. It has generated massive amounts of funding to support continued scaling and R&D efforts.
You'll be working on
- Lead the future of physical AI in defense by building the reasoning models for unmanned autonomous systems, across aerial, maritime, land, and space domains
- 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 with world-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
- 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
Benefits
- Excellent Medical Coverage
- Mental Health Employee Assistance Program
- Paid Parental Leave
- Pet Insurance
- Flexible Work Hours
- Onsite Gym (DC)
- Gym Discount (San Diego)
- Free Parking
- Competitive Compensation
- Stock Benefits
- 401K Services and Match
We're looking for
- 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
- 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
- Familiarity with NVIDIA Jetson and other edge compute platforms (e.g. Qualcomm, AMD, etc.)
- Fluency in traditional AI/ML techniques and an ability to combine them thoughtfully with learned models
- Strong command of training and adaptation approaches such as self-supervised learning, supervised fine-tuning, imitation learning, reinforcement learning, and distillation
- 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
- 5+ years of people management experience leading engineering or applied research teams, with demonstrated success hiring, coaching, developing technical leaders, and managing performance
- Strong familiarity with traditional robotics algorithms, e.g. path planning, probabilistic modeling, optimization approaches, etc
- Experience with multimodal, embodied, robotic, or autonomous-system models; familiarity with VLA models and world models is particularly relevant
- Deep expertise in foundation models, modern deep learning, model architectures, and the training ecosystem
- Experience defining datasets, training objectives, experimental workflows, benchmarks, and reproducible criteria for comparing models
- Experience with training frameworks such as PyTorch, JAX, TensorFlow, etc
- Experience with unmanned autonomous systems in defense applications
- Experience with simulation-to-real on autonomous systems
- PhD in Computer Science, Electrical Engineering, or similar degree
- Expertise in optimising and deploying ML models to resource-constrained hardware platforms
- Experience managing managers or leading multiple engineering teams
- 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