Staff Engineer, Hivemind Reasoning (R6292)

Shield AI

San Mateo, CA

On-site

Who this role is best for

A natural match if you have experience with foundation models and deploying ML on edge devices.

Best fit for

Candidates with advanced ML experience and a background in robotics or autonomous systems
— “Experience with training frameworks such as PyTorch, JAX, TensorFlow, etc.”
Individuals who have worked on model adaptation and simulation-to-real deployment
— “Establish rigorous evaluations in simulation and with real-world data”

Things to consider

Background checks and reference checks are standard prerequisites for employment
— “All offers are contingent on a cleared background and possible reference check”
The role requires fluency in both traditional and modern AI/ML methods
— “Fluency in traditional AI/ML techniques and an ability to combine them thoughtfully with learned models”

How to stand out

Highlight experience with multimodal or autonomous systems in your resume and interview stories
— “Experience with multimodal, embodied, robotic, or autonomous-system models”
Demonstrate your ability to define training objectives and benchmarks in your application materials
— “Experience defining datasets, training objectives, experimental workflows, benchmarks, and reproducible criteria for comparing models”
Emphasize your hands-on contributions to model design and deployment in your interview responses
— “A demonstrated track record of developing and deploying advanced ML models in production environments”
Showcase your knowledge of edge compute platforms and model optimization techniques
— “Familiarity with NVIDIA Jetson and other edge compute platforms (e.g. Qualcomm, AMD, etc.)”
Demonstrate your ability to evaluate models under changing conditions and team dynamics
— “Establish rigorous evaluations in simulation and with real-world data, measuring generalization, robustness, uncertainty, and performance as team size and operating conditions change”

Pace · Fast PacedCollaboration · HighAutonomy · MediumDecision Impact · Team

Derived from job-description analysis by Serendipath's career intelligence engine.

What success looks like

Developed and deployed advanced ML models in production environments
Designed and implemented reasoning and cognition models for autonomous platforms
Established rigorous evaluations in simulation and real-world data
Delivered trained models with clear performance evidence

Typical background

PhD or MS in Computer Science, Electrical Engineering, Mechanical Engineering, Aerospace Engineering7+ years of experience in developing and deploying ML models

Skills & requirements

Required

Advanced Machine LearningDeep LearningModel Training And EvaluationMultimodal ModelsAutonomous SystemsReinforcement LearningSelf-supervised LearningReproducible WorkflowsData Requirements DefinitionModel Architecture DesignModel AdaptationModel TradeoffsModel Failures DiagnosisFeedback LoopsCommunication Of Technical Tradeoffs

Preferred

Vision-language ModelsVision-language-action ModelsTraditional Ai/ml TechniquesRobotics AlgorithmsPath PlanningProbabilistic ModelingOptimization

Stack & domain

PythonReactTensorFlowJAXPyTorchSelf-supervised LearningSupervised Fine-tuningImitation LearningReinforcement LearningDistillationVision-language Models (vlms)Vision-language-action Models (vlas)Traditional Ai/ml MethodsMultimodalEmbodiedRoboticAutonomous-system ModelsLeadershipCommunicationProblem-solvingTeamworkDefense-techAutonomy SoftwareAircraftSimulationSynthetic Reality Technologies

About the role

Original posting from Shield AI via Lever

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-BAT aircraft, 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.
What you'll do::
Design, implement, train, adapt, and evaluate reasoning and cognition models for unmanned autonomous platforms.
Make and validate tradeoffs among foundation-model approaches, including vision-language models (VLMs) and vision-language-action models (VLAs), specialized deep-learning models, and traditional AI/ML methods.
Develop capabilities for interpreting context, reasoning over time, coordinating among agents, and adapting to incomplete or changing information.
Build reproducible training and adaptation workflows using appropriate combinations of self-supervised learning, supervised fine-tuning, imitation learning, reinforcement learning, and distillation.
Define data requirements, training objectives, benchmarks, and evaluation methods that support meaningful comparisons among model approaches.
Establish rigorous evaluations in simulation and with real-world data, measuring generalization, robustness, uncertainty, and performance as team size and operating conditions change.
Incorporate edge constraints into model architecture and training decisions in partnership with hardware, autonomy, and inference teams.
Diagnose model failures and create feedback loops among data curation, training experiments, and re-evaluation.
Deliver trained models, reproducible results, and clear performance evidence to partner teams.
Communicate technical tradeoffs, experimental findings, and model limitations clearly to research, engineering, product, and organizational leadership.
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 7 years of related experience with a Bachelor’s degree; or 6 years and a Master’s degree; or 4 years with a PhD; or equivalent work experience.
A demonstrated track record of developing and deploying advanced ML models in production environments, with substantial hands-on contributions to model design, training, and evaluation.
Deep expertise in foundation models, modern deep learning, model architectures, and the 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.
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::
MS or 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 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.
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.
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Shield AI is proud to be an equal opportunity workplace and is an affirmative action employer. We are committed to equal 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.

Source: Shield AI careers (Lever)