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Prime Intellect · 3 months ago

Applied Research - RL & Agents

San Francisco

Full-time

Onsite

Mid Level

Prime Intellect is building the open superintelligence stack, enabling researchers and enterprises to run end-to-end reinforcement learning at scale. The role involves designing advanced AI agents, developing robust infrastructure, and bridging customer needs with research priorities.

Artificial Intelligence (AI)Cloud Computing

H1B Sponsored

Responsibilities

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

Building Robust Infrastructure: Developing the distributed systems and coordination frameworks that enable these agents to operate reliably, efficiently, and at massive scale

Bridge Between Customers & Research: Translate customer needs into clear technical requirements that guide product and research priorities

Prototype in the Field: Rapidly design and deploy agents, evals, and harnesses alongside customers to validate solutions

Customer-Facing Engineering: Work side-by-side with customers to deeply understand workflows and bottlenecks

Prototype agents and eval harnesses tailored to real use cases, then hand off hardened systems to core teams

Translate customer insights 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

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/inference pipelines, ensuring scalability and cost efficiency

Develop observability and monitoring (Prometheus, Grafana, tracing) to ensure reliability and performance in production deployments

Qualification

Machine Learning EngineeringReinforcement LearningDistributed TrainingContainerizationResearch ContributionsCustomer-Facing SkillsWorkflow AutomationMonitoringDecision-MakingObservability