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Selected role

Applied AI Engineer (Spatial and Embodied AI)

Medal York, WA, US

$150k - $350k Senior Full-time

Location

New York City, Washington DC

Employment Type

Full time

Location Type

On-site

Department

General Intuition

Compensation

$150K – $350K • Offers Equity

The compensation may vary further depending on individualized factors for candidates, such as job-related knowledge, skills, experience, and other objective business considerations.

Overview

The Company: General Intuition

Today’s most powerful foundation models are trained on written words. But human intelligence extends far beyond language. Truly intelligent machines must move from words to worlds, developing the ability to perceive, anticipate, and act within complex environments.

We believe games represent the highest density expression of ingenuity and problem-solving. General Intuition builds on Medal, the world’s largest and fastest-growing platform for gaming moments. Each year, players capture billions of gameplay clips across countless environments, producing uniquely rich data for learning systems.

Over the past year, we’ve been advancing the frontier across:

agents capable of deep spatial and temporal reasoning,
world models and simulation environments for training and evaluation, and
video understanding with an emphasis on generalization beyond games.

The Role

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.

Key Responsibilities

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.

Qualifications

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 (TensorRT, 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 engineering or DevSecOps experience in regulated industries, including degraded, intermittent, limited) networking constraints
Open-source contributions or demonstrable applied systems work, or a portfolio of "side projects" that demonstrate AI/ML, and engineering curiosity

The Stack

ML & Research: Python, PyTorch, NumPy, OpenCV, Triton, CUDA for large-scale training, real-time inference, and applied CV/ML
Pipelines & Experimentation: Kubeflow Pipelines and Airflow with continuous evaluation, A/B testing, and performance monitoring across training and production
Backend & Systems: Java services with Redis and RabbitMQ, plus performance-critical C++ components; containerized with Docker and Kubernetes on GCP or on-prem
Clients & Edge Software: Electron/React desktop apps, C# and C++ high-performance recorders, and mobile clients in Swift (iOS) and Kotlin (Android)
Infra, Hardware & Other Deployments: Terraform-managed infrastructure, CI/CD via GitHub Actions and CircleCI; deployment to NVIDIA GPU clusters, air-gapped or on-prem environments, hardened Linux systems (FIPS/STIG), and constrained real-world hardware requiring model optimization, hardware-specific acceleration, and secure supply-chain practices

Benefits

Competitive salary and meaningful equity
Comprehensive health insurance including dental and vision insurance
401k

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