Applied Research - Evals & Data

OWN YOUR INTELLIGENCE Prime Intellect is building the open superintelligence stack: the infrastructure frontier AI labs build internally, made available to every ambitious AI team. Our platform, Lab, unifies compute, environments, evaluations, secure sandboxes, high-performance training, and deployment into one full-stack system for post-training at frontier scale - from SFT and RL to tool use, agent workflows, and continuously improving production models. We are building open frontier AI: open-source models trained end to end for long-horizon tasks like autonomous research, and the full-stack platform our own research team uses to build them. The next generation of AI companies, enterprises, and research teams do not just need more GPUs. They need the ability to turn their own workflows, tools, data, and feedback loops into superintelligence they own. Prime Intellect has raised $150M in total funding from Founders Fund, Radical Ventures, NVIDIA, and exceptional AI, infrastructure, and enterprise operators — including Andrej Karpathy, Dwarkesh Patel, and leaders and founders from Ramp, Perplexity, Harvey, Mercor, Zapier, Datadog, Cognition, OpenAI, Thinking Machines, Together AI, SemiAnalysis, LangChain, Browserbase, Cloudflare, Sierra, Databricks, Airbnb, OpenRouter, Standard Intelligence, Fleet, Core Auto, and more. We are looking for people who want to build at the intersection of frontier research, real infrastructure, and go-to-market for a category that does not fully exist yet. Role Impact This is a customer facing role at the intersection of cutting-edge RL/post-training methods, applied data, and agent systems. You’ll have a direct impact on shaping how advanced models are aligned, evaluated, deployed, and used in the real world by: - Advancing Agent Capabilities: Designing and iterating on next-generation AI agents that tackle real workloads—workflow automation, reasoning-intensive tasks, and decision-making at scale. Working with applied data from real deployments to continuously refine policies, improve reasoning, and enhance reliability and safety. - Building Robust Infrastructure: Developing the distributed systems, evaluation pipelines, and coordination frameworks that enable these agents to operate reliably, efficiently, and at massive scale. Building data capture, processing, and versioning workflows for feedback, model traces, and reward signals. - Bridge Between Customers & Research: Translating customer needs and insights from applied data into clear technical requirements that guide product and research priorities. Collaborating closely with RL and eval teams to ensure real-world signals inform model alignment and reward shaping. - Prototype in the Field: Rapidly designing and deploying agents, evals, and harnesses alongside customers to validate solutions. Using applied evaluation data to iterate on model performance and discover new capabilities. Customer-Facing Engineering - Work side-by-side with customers to deeply understand workflows, data sources, and bottlenecks. - Prototype agents, data pipelines, and eval harnesses tailored to real use cases, then hand off hardened systems to core teams. - Translate customer insights and evaluation results into roadmap and research direction. Post-training & Reinforcement Learning - Design and implement novel RL and post-training methods (RLHF, RLVR, GRPO, etc.) to align large models with domain-specific tasks. - Build evaluation harnesses and verifiers to measure reasoning, robustness, and agentic behavior in real-world workflows. - Integrate applied data collection and analytics into the post-training process to surface regressions, emergent skills, and alignment opportunities. - Prototype multi-agent and memory-augmented systems to expand capabilities for customer-facing solutions. Agent Development & Infrastructure - Rapidly prototype and iterate on AI agents for automation, workflow orchestration, and decision-making. - Extend and integrate with agent frameworks to support evolving feature requests and performance requirements. - Architect and maintain distributed training and inference pipelines, ensuring scalability and cost efficiency. - Develop observability and monitoring (Prometheus, Grafana, tracing) to ensure reliability and performance in production deployments. Requirements - Strong background in machine learning engineering, with experience in post-training, RL, or large-scale model alignment. - Experience with applied data workflows and evaluation frameworks for large models or agents (e.g., SWE-Bench, HELM, EvalFlow, internal eval pipelines). - Deep expertise in distributed training/inference frameworks (e.g., vLLM, sglang, Ray, Accelerate). - Experience deploying containerized systems at scale (Docker, Kubernetes, Terraform). - Track record of research contributions (publications, open-source contributions, benchmarks) in ML/RL. - Passion for advancing the