Staff Engineer, Hivemind Reasoning (R6292)

San Mateo · USD 240,000-360,000 / year · Onsite · Posted Oct 15, 2026

At a glance

The Staff Engineer, Hivemind Reasoning will design, implement, train, and evaluate reasoning and cognition models for unmanned autonomous platforms.
This role involves making tradeoffs between foundation-model approaches and traditional AI/ML methods to develop capabilities for context interpretation and agent coordination.
The engineer will work in partnership with hardware, autonomy, and inference teams to incorporate edge constraints into model architecture.

Design, implement, train, adapt, and evaluate reasoning and cognition models for unmanned autonomous platforms.
Make and validate tradeoffs among foundation-model approaches, including VLMs, 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 self-supervised learning, supervised fine-tuning, imitation learning, reinforcement learning, and distillation.
Define data requirements, training objectives, benchmarks, and evaluation methods.
Establish rigorous evaluations in simulation and with real-world data, measuring generalization, robustness, and uncertainty.
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 to leadership.

BS/MS in Computer Science, Electrical Engineering, Mechanical Engineering, Aerospace Engineering, or similar degree, or equivalent practical experience.
Demonstrated track record of developing and deploying advanced ML models in production environments.
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.
Fluency in traditional AI/ML techniques and ability to combine them with learned models.
Experience with training frameworks such as PyTorch, JAX, or TensorFlow.
Experience with multimodal, embodied, robotic, or autonomous-system models.
Familiarity with traditional robotics algorithms such as path planning, probabilistic modeling, and optimization.
Familiarity with NVIDIA Jetson and other edge compute platforms.

MS or PhD in Computer Science, Electrical Engineering, or similar degree.
Expertise in optimizing 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, such as rigid body dynamics and sensor/actuator models.
Familiarity with VLA models and world models.

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