Job Description
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Company Overview
A frontier artificial intelligence company is building an open superintelligence stack designed to provide AI teams with the infrastructure needed to develop and operate advanced AI systems. Its full-stack platform brings together compute, environments, evaluations, secure sandboxes, high-performance training, and deployment capabilities for post-training at frontier scale.
Key Responsibilities
Customer Engagement & Technical Delivery
- Embed directly with strategic customers to understand their agent architectures, failure modes, workflows, and product objectives.
- Design and build custom reinforcement learning environments, evaluation harnesses, and verifiers that define measurable outcomes for customer-specific domains.
- Architect agent scaffolding involving tool use, multi-step reasoning, memory, and sandbox execution based on customer workflows.
- Configure and launch training runs on the platform while iterating on reward functions, rollout strategies, and evaluation criteria.
- Serve as the technical lead for customer engagements from initial discovery through model deployment and improvement.
Platform Feedback & Ecosystem
- Identify repeatable patterns from customer engagements and turn them into reference implementations, templates, and documentation.
- Represent customer needs internally and provide technical input into the development roadmap for the platform, verifiers, environments, and training infrastructure.
- Build high-quality examples and technical recipes that help customers and open-source contributors extend the platform.
- Contribute to technical content, including blog posts, tutorials, and case studies demonstrating real-world platform applications.
Applied Research & Experimentation
- Develop evaluation methodologies for agentic behavior, including multi-step reasoning, tool-use correctness, failure recovery, and long-horizon task completion.
- Prototype and iterate on agent harnesses for real-world applications such as code generation, workflow automation, and document processing.
- Experiment with reward design, rubric construction, and environment shaping to improve training signal quality.
- Monitor developments in agentic AI, evaluation methodologies, and post-training techniques and apply relevant advances to customer engagements.
- Translate research concepts into practical solutions for real-world AI workloads.
Required Qualifications
- Hands-on experience building, evaluating, or deploying LLM-based agents within the past 1–2 years.
- Practical understanding of production AI systems, including common agent failure modes and effective evaluation approaches.
- Strong understanding of evaluation design, including defining measurable outcomes, constructing rubrics, and identifying weaknesses in reward signals.
- Working knowledge of reinforcement learning and post-training concepts such as GRPO, RLHF, reward modeling, and supervised fine-tuning.
- Strong Python programming skills.
- Experience with modern AI development technologies, including Hugging Face, inference engines, and agent frameworks.
- Experience in a customer-facing, consulting-oriented technical role, or experience as a technical founder.
- Ability to work directly with engineering teams and translate technical requirements into practical solutions.
- Excellent written and verbal communication skills.
- Ability to create clear technical specifications, case studies, documentation, and customer communications.
- High level of initiative and comfort working in ambiguous environments.
- Ability to independently scope problems, develop solutions, ship implementations, and iterate based on results.
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