Own applied RL programs from ambiguous customer problem to deployed improvement loop.
Understand workflows, build environments and evaluations, run post-training experiments, and deploy results.
Work directly with customer researchers, engineers, and leaders, accountable for research result and practical impact.
Prime Intellect builds the open superintelligence stack, providing a full-stack platform for frontier-scale post-training of AI models. The company has raised $150M from top investors and is seeking builders who want to work at the intersection of frontier research, real infrastructure, and go-to-market.
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.
Build quantitative-research environments, agents, and post-training methods for systematic decision-making.
Turn workflows into tasks, run experiments against real baselines, and deploy what works.
Work directly with customers and quantitative funds while maintaining a substantive research agenda.
Prime Intellect builds the open superintelligence stack for post-training AI, unifying compute, environments, evaluations, and deployment. They have raised $150M from top investors and are building a culture of ambitious, externally-facing applied research.
Design and build challenging evaluations and executable environments for coding and long-horizon agents.
Develop scoring logic and verifiers to detect reward hacks and trace failures through model outputs and harness code.
Build and scale RL data pipelines for generating and curating challenging coding and agentic tasks.
Prime Intellect is building the open superintelligence stack, providing infrastructure for post-training frontier AI models and open-source models trained for long-horizon tasks. The company has raised $150M from top investors and is a mission-driven team passionate about accelerating science and AI.
Build reinforcement learning and agent environments for real customer and Frontier lab use cases, including task specifications, scoring, and evaluation.
Develop benchmarks and evaluation harnesses to measure model and data quality across accuracy, robustness, safety, latency, and cost.
Run fine-tuning, adapter, and other model experiments to evaluate how data and methods influence model behavior, and deploy local or self-hosted models for evaluation and inference.
Appen has been a leader in AI training data for over 30 years, specializing in human-generated data to train, fine-tune, and evaluate models across generative AI, LLMs, computer vision, and speech recognition. They support model development through an AI-assisted data annotation platform and a global crowd of over 1 million contributors in more than 200 countries, fostering a culture of innovation, collaboration, and humility over ego.
Learn, maintain, and improve our synthetic data pipeline and automated training system.
Take over a model and improve it with an agent loop that generates its own data.
Build closed loops where agents train detectors, test against validation sets, and improve with little human input.
CVEDIA builds computer vision models and applications that run on server and edge hardware using synthetic data. We are a small, fully remote team that uses AI agents across the company.
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 RL environments, agentic systems, LLM pipelines, and evaluation frameworks for real-world AI use cases.
Develop benchmarks and evaluation harnesses to assess model quality across accuracy, safety, latency, and cost.
Conduct fine-tuning and model experiments, deploy self-hosted models, and document reproducible methodologies.
The employer is an organization focused on applied AI research, building practical and reusable AI systems for real-world use cases. It values curiosity, accountability, innovation, collaboration, and continuous learning in a remote environment.
Building agents, environments, and post-training systems for real semiconductor design and verification workflows.
Designing rewards and evaluations that combine functional correctness with tool efficiency, robustness, and performance/power/area constraints.
Owning interactive environments where agents use EDA tools to modify designs, debug failures, and plan multi-step work.
Prime Intellect builds the open superintelligence stack, unifying compute, environments, evaluations, and deployment for frontier-scale AI post-training. The company has raised $150M in funding from top investors like Founders Fund and NVIDIA, fostering a culture of building at the intersection of research, infrastructure, and go-to-market.
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.
Design, build, and deploy AI agents that automate Mind Moves' internal operations, from contractor onboarding to market-rate research.
Decompose workflows into agent architectures—skills, tools, evals, memory, and orchestration—and build evaluation frameworks that measure and improve agent quality.
Own the full lifecycle from identifying workflows to prototyping, deployment, monitoring, and iteration, creating reusable infrastructure and documentation.
Mind Moves is a women-owned Washington, D.C.-based consultancy that helps businesses and governments design, build, and deploy human-in-the-loop AI solutions. It is a small, entrepreneurial team of AI pioneers that has delivered products serving millions of users and worked with Fortune 500 corporations and large government entities.
Develop and deploy cutting-edge AI/ML solutions to enhance the platform and improve student learning experiences.
Design, develop, and optimize LLM-powered agentic systems and APIs for real product use cases.
Collaborate with senior engineers and contribute to evaluation frameworks and MLOps pipelines.
Interview Kickstart specializes in interview preparation and career transitions into high-demand tech fields like AI, ML, and Data Science. Over 17,000 tech professionals have been guided by current and former hiring managers to land coveted positions at companies like Google and Amazon.
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.
Building multi-agent systems and implementing agent-based workflows and AI automation solutions.
Developing and optimizing prompt engineering strategies, including evaluation and iteration.
Integrating LLMs and AI models into cloud-based applications and APIs while researching new AI tools.
Software Mind develops impactful software solutions for global companies, partnering with tech giants and unicorns on transformative projects. The company builds cross-functional engineering teams and embraces a culture of openness, respect, grit, and cooperation.
Design agent architectures for planning, reasoning, tool use, and memory integration.
Improve reliability on long-running tasks with failure recovery and evaluation systems.
Build loops for agents to improve with real use, balancing quality, latency, and cost.
Adaption builds AI systems that evolve in real-time, making them flexible and personalized. They are a global-first team focused on talent density and collaboration.
Own agent quality end-to-end for Bliro's chat and voice agents, including prompting, context, and model evaluation.
Build eval sets, LLM judges, and benchmarks to measure quality, latency, and cost with statistical rigor.
Establish a repeatable process for improving agent quality and set the long-term technology roadmap.
Bliro builds an AI assistant that handles desk work for field sales teams, with over one million customer touchpoints documented. Backed by leading investors and trusted by German Mittelstand companies, it's a small, high-performing team that values ownership and continuous improvement.
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.
Own the path from emerging research artifact to excellent open-source and product experience.
Build core open-source systems and shape researcher-facing features in our training platform.
Work with external researchers and contributors to turn fast-moving abstractions into stable public interfaces.
Prime Intellect is building the open superintelligence stack, providing infrastructure and a full-stack platform for post-training frontier AI models. The company has raised $150M in funding from prominent investors and aims to make open-source AI research fully reproducible and accessible.
Partner with Platform Engineering, Infrastructure, and Developer Productivity to integrate agent capabilities.
Reddit is a community of communities built on shared interests, passion, and trust, with 100,000+ active communities and ~130 million daily active users. We are one of the internet's largest sources of information, fostering open and authentic conversations.
Build LLM-based agents on the platform's scaffolding, integrating tool calls, internal APIs, and guardrails.
Take agents to production on AWS with containers, CI/CD, secrets, permissions, and security controls.
Define and run evals, monitor with Langfuse, and document runbooks for independent operation.
Muttdata builds innovative Data Products and Machine Learning solutions to help companies solve complex business challenges. It is a fast-growing, remote-first startup that values collaboration, continuous learning, and a positive, ownership-driven culture.