Build simulation and real-world environments for training and evaluating embodied agents.
Train long-horizon robots with RL, imitation learning, and world models under hardware constraints.
Run post-training experiments to find what works and share reusable open-source systems.
Prime Intellect builds the open superintelligence stack, making frontier AI infrastructure available to ambitious AI teams. With $150M in funding from top investors, they are a well-funded startup focused on open-source AI and decentralized training.
Develop and improve AI methods, algorithms, and systems across the lifecycle of reliable agents.
Create new approaches for simulating, evaluating, and optimizing agent behavior in real-world settings.
Turn research ideas into working systems, prototypes, and production-facing capabilities.
The company is a research-focused team developing methods and systems to make AI agents more reliable in production. It operates as a small team with a culture of original research, experimentation, and close collaboration with product and engineering.
Develop RL algorithms for reasoning, coding, and long-horizon tasks.
Design and run scaling experiments to improve training recipes across model sizes.
Extend RL training frameworks for asynchronous workflows and reliability.
Prime Intellect builds the open superintelligence stack, offering a platform that unifies compute, training, and deployment for frontier AI teams. With $150M in funding and a mission-driven culture, they focus on open-source AI research and real infrastructure.
Develop and improve AI methods, algorithms, and systems across the lifecycle of reliable AI agents.
Create new approaches for simulating, evaluating, and optimizing agent behavior in real-world settings.
Turn research ideas into working systems, prototypes, and production-facing capabilities.
The company builds infrastructure that helps enterprises make AI agents more reliable in production. It is a technical and research-focused team working on AI methods and practical systems.
Develop AI methods and algorithms for reliable agents.
Design simulations and evaluations for agent behavior.
Partner with product and engineering to ship research.
The company builds an AI agent platform focused on making agents reliable in production. It is a research-driven environment with a collaborative, startup-oriented culture.
Train models and evaluate AI systems to deliver world-class AI to the warfighter.
Run experiments in reinforcement learning, pretraining, multimodal training, and related areas.
Work as a self-starter in a fast-paced Series-A startup, interacting with customers and owning projects.
EdgeRunner AI builds secure, edge-deployed AI that helps American warfighters make faster decisions and interact with robotic platforms using natural language. It's a Series-A startup with a remote-first culture and a team of researchers and ML engineers.
Design, build, and maintain robotic platforms, including sensor integration, calibration, and actuator control.
Develop the robotics software stack for perception, state estimation, motion planning, and control, plus simulation environments.
Deploy and optimize learned models on hardware with attention to latency, safety, and reliability.
Metacognition builds intelligent systems capable of learning, remembering, and reasoning over time. It is an early-stage startup with a highly collaborative, fast-moving environment where employees have meaningful ownership.
Coordinate real-time robot software, motion planning systems, and machine learning pipelines across multiple platforms.
Build and maintain data-labeling tools, perception systems, and compilers for robotics applications.
Deploy software in customer facilities and improve performance based on real-world operation.
They build software for AI-driven robots in production environments. As a Series A company, they maintain an early-stage culture that values ownership and adaptability.
Design and implement state-of-the-art ML models and training pipelines for robotics.
Develop efficient data/training strategies and evaluation frameworks for rapid experimentation.
Collaborate with engineering to optimize training infrastructure and deployment.
We're revolutionizing real-world automation by making robotic systems accessible to everyone. Our AI-powered platform brings software automation to physical spaces, and we're a small startup team working across the stack to solve customer problems.
Drive end-to-end program management for complex, cross-functional AI and autonomy initiatives with engineering teams and external partners.
Act as a technical thought partner, translating between business stakeholders, R&D engineers, and partner teams on AI systems, LLMs, and agentic frameworks.
Establish success criteria, manage risks, and optimize processes to deliver measurable program value across the AI & Autonomy function.
Rockwell Automation is a global technology leader in industrial automation and digital transformation, helping manufacturers be more productive and sustainable. With approximately 28,000 employees across more than 100 countries, they foster a culture of problem solvers and forward thinkers.
Oversee onsite testing and preparation of robot fleets.
Coordinate cross-functional teams to define project scope, milestones, and deliverables.
Lead project planning, resource allocation, and scheduling for timely deployment.
Collaborative Robotics develops collaborative robots to redefine human-robot interaction, aiming to make robots a trusted extension of their surroundings. As a team of innovators and builders, they foster a fast-paced, collaborative culture focused on pushing technological boundaries.
Build and post-train agents for scientific research, including literature search, code execution, and domain tool use.
Develop RL and multi-agent methods for proposing, testing, and critiquing hypotheses, with strong baselines and evaluation.
Design verifiers, reward signals, and reproducible experimental loops for scientific workflows, ensuring evidence-based improvements.
Prime Intellect builds the open superintelligence stack, providing infrastructure for frontier AI labs to train, evaluate, and deploy models at scale. The company has raised $150M from top investors and operators, and is building a culture focused on frontier research, real infrastructure, and go-to-market for a new category.
Research, develop, and maintain production-quality machine learning code for materials science problems.
Collaborate with product, engineering, and research teams to transform AI ideas into scalable, customer-ready capabilities.
Evaluate and improve model performance, reliability, and interpretability across the full feature lifecycle.
The company develops materials-aware AI capabilities for scientific and industrial applications. They value autonomy, collaboration, and continuous learning in a remote environment.
Build the Learning Gym, a sandboxed environment for training and evaluating AI agents on real Niural workflows with ground-truth reward signals.
Engineer verifiers, run reinforcement learning loops, and establish honest baselines to prove agent improvements generalize.
Write first-author research papers and internal technical reports, and represent the work externally through preprints and talks.
Niural is a global payroll, Employer of Record (EOR), Agent of Record (AOR), and Contractor Management platform. It is a relentlessly ambitious team focused on building foundational internet infrastructure for the AI era.
Build and optimize RL infrastructure for large-scale training.
Improve efficiency across compute, memory, networking, and scheduling.
Design low-level optimizations, from kernels to runtime.
Prime Intellect builds the open superintelligence stack, providing full-stack infrastructure for frontier-scale AI post-training and deployment. The company has raised $150M from top investors and is mission-driven, focused on open-source AI and accelerating science.