Applied AI Engineer; Spatial and Embodied AI

The Company:
General Intuition

We are the frontier research lab dedicated to building foundation models for environments that require deep spatial and temporal reasoning. For the past year, we've been pushing the forefront of AI across agents capable of navigating space and time, world models that provide training environments for those agents, and video understanding models with a focus on transfer to the real world.

We raised a seed round of $133M from General Catalyst and Khosla to discover the next generation of intelligence.

We are looking for an Applied AI Engineer to connect our research with the reality of our partners’ environments that are constrained by hardware, power, and real world interference.

We work with customers operating in complex areas across robotics, simulation, aerospace and defense, manufacturing, logistics, industrial automation, and more. You will be responsible for taking our model, focusing on post-training, evaluation data, and integrations to ensure our customer's platforms work regardless of a messy, constrained tech stack and hardware.

You will embed with our partners to understand their actual problems, not just what they put in an RFP. You’ll look at their legacy control systems, latency challenges, and power needs and figure out how our AI helps them achieve something that was never possible before.

You'll be a key part of the feedback loop. When a model fails because of sensor noise or unexpected physics, you don't just log a bug. You figure out why, and you work across our team to fix the underlying architecture. You ensure we are building technology that survives contact with the real world, for years to come.

We're looking for a technical polyglot. You might have started in systems engineering, physics, or neuroscience and moved to ML, or the other way around. You know Python and PyTorch, but you aren't afraid of C++ or low-level hardware constraints.

Most importantly, you have high agency and want to be a part of an amazing team.

Applied AI/ML Engineering & Mission Ownership

• Embed with partners to solve the problems with our frontier AI/ML tools, informing our research and product development plan along the way…not just deploy software.

• Be the primary filter between the messy reality of the physical world and our research and technical staff, surfacing real commercial challenges and pain-points.

• Build and tune models, prototype, script, and patch (often in the field), turning ambiguous requirements into executable code.

Systems Integration & Edge Compute

• Build the connective tissue between our AI and the customer's reality, and then help them rethink the art of the possible. This means writing high-performance code (C++/Go/Python) that integrates our inference engine with legacy sensors, RTOS, and diverse hardware peripherals.

• Optimize complex ML models for survival in harsh computing environments.

• Leverage our simulation and world-model capabilities to validate operational plans before they touch physical hardware.

Technical Diplomacy

• Translate the probabilistic nature of AI into the deterministic language of industrial control systems and mission operators.

• Explain trade-offs to non-technical individuals and deep technical details to systems engineers, building the trust required to deploy autonomous systems in critical paths.

Required

• 5+ years experience taking complex systems from prototype to production, within software engineering or applied AI/ML

• Strong experience in the ML stack (Python, Docker, Kubernetes, infrastructure-as-code, and CI/CD for ML pipelines) with competent systems programming skills (C++, Go, Rust, or Java), and ability to use modern AI coding tools

• Strong applied machine learning experience, specifically in the lifecycle of deploying, evaluating, and debugging models

• Experience in at least one of the following, with working knowledge of the others:

  • Agents or policy learning (e.g., RL, planning, control theory, spatial reasoning)

  • World models, simulation environments (Unity/Unreal, Omniverse, Isaac Sim), or model-based learning

  • Perception, sensor fusion, or inverse dynamics models (IDMs)

• Exposure to bridging the "hardware-software" gap: integrating AI inference with sensors, edge devices, RTOS, or legacy industrial networks

• Full-stack systems mindset: understanding of memory management, concurrency, networking, and APIs

• U.S. citizenship and ability to obtain and maintain a national security clearance (TS/SCI preferred)

• Ability to comply with export control requirements (ITAR/EAR)

Preferred

• Experience, and comfort in, forward-type environments often found with partners across the industrial base, defense, intelligence, aerospace, and robotics environments at the edge

• Edge AI, inference optimization, or deployment in constrained settings (Tensor
  
  RT, ONNX, or mobile inference as examples)

• Background in autonomous systems, control, or real-time systems
  Startup or early-stage engineering experience

• Understanding of secure systems…