Research Engineer (Mountain View) - 17813

Location: Mountain View, CA
Type: Full-time
Compensation: Competitive salary + equity
Benefits: Health coverage, ownership upside, and direct collaboration with leading AI research organizations

We are a fast-growing applied AI research lab focused on data and reinforcement learning (RL) environment curation for training and evaluating advanced agents.

Our work has powered state-of-the-art reasoning datasets, specialized frontier models, and multi-turn, tool-using agents trained via reinforcement learning. Our research and systems are actively used by multiple top-tier AI labs and enterprise teams.

By combining deep research expertise with production-grade engineering, we are building the infrastructure layer that enables the next generation of agent training. We are uniquely positioned to capture significant market share in data-centric AI and RL environment design.

We are seeking a Research Engineer to operate at the intersection of cutting-edge agent research and production-scale systems.

In this role, you will work closely with frontier AI labs, enterprise customers, and internal research teams to design, build, and deploy high-quality RL environments at scale. You’ll translate research insights into robust, reproducible pipelines that directly impact how modern agents are trained and evaluated.

This position is ideal for someone who enjoys:

• Reading and implementing new research

• Prototyping novel ideas quickly

• Scaling research artifacts into production systems

• Working directly with highly technical external partners

You will have real ownership over systems that shape how next-generation agents learn.

• Partner with frontier AI labs to understand agent training requirements and design custom RL environments

• Stay current with advances in reinforcement learning, agent training, curriculum design, and evaluation

• Prototype and validate new approaches to environment generation and data curation

• Translate academic research into scalable engineering solutions

• Build and maintain scalable pipelines for creating, validating, and deploying RL environments

• Design systems that ensure data quality, diversity, and reproducibility

• Implement automated QA and verification processes for environments

• Develop evaluation frameworks to measure environment effectiveness and training outcomes

• Work directly with enterprise customers to understand domain-specific agent challenges

• Customize environment suites, benchmarks, and evaluation setups for different use cases

• Provide technical guidance on best practices for agent training and evaluation

• Present research findings and system capabilities to technical stakeholders

• Scale research prototypes into reliable, production-ready systems

• Establish reproducible workflows and strong engineering standards

• Create documentation and tooling for internal teams and external users

• Monitor, optimize, and evolve systems as environment production scales

• MS or PhD in Machine Learning, Computer Science, or a related field — or equivalent industry research experience

• Demonstrated research contributions (publications, open-source work, or deployed research systems)

• Strong understanding of reinforcement learning, agent training, or related fields

• Ability to read, implement, and adapt ideas from recent research papers

• Strong Python skills and experience with ML frameworks (e.g., PyTorch, JAX)

• Experience building research infrastructure or production ML systems

• Familiarity with cloud platforms (AWS, GCP) and distributed systems

• Systematic approach to testing, validation, and quality assurance

• Comfortable leveraging modern developer tools (e.g., AI-assisted coding workflows)

• Excellent communication skills across research and engineering teams

• Ability to translate research concepts into practical system requirements

• Strong project scoping, prioritization, and execution skills

• Comfortable presenting technical work to diverse, highly technical audiences

• Understanding of what makes research artifacts valuable in real-world use

• Experience shipping datasets, benchmarks, tools, or platforms used by others

• Attention to documentation, usability, and long-term maintainability

• Customer-oriented approach to solving technical problems

• Hands-on experience training or evaluating RL agents

• Background in data-centric AI, synthetic data, or dataset creation

• Publications at top-tier ML conferences (NeurIPS, ICML, ICLR, etc.)

• Prior experience as a Research Engineer or Applied Scientist

• Contributions to widely used datasets, benchmarks, or evaluation suites

• Work directly with some of the most advanced AI research teams in the world

• Influence how next-generation agents are trained and evaluated

• Operate at the rare intersection of frontier research and production systems

• High ownership, real-world impact, and meaningful equity upside