Member of Technical Staff - Platform Engineering

Location

San Francisco, CA

Fully on-site — 5 days per week in-office.

Company Stage of Funding

Early Stage — $8.5M raised

Office Type

On-site

Salary

$200,000 – $250,000 Base + 0.15% – 0.30% Equity

Visa

Open to Visa Transfers — H-1B transfers, TN, and STEM OPT supported.

Travel

No significant travel requirement specified.

Company Description

Our client is an early-stage AI company building fully managed environments and benchmarks for training and evaluating advanced computer agents.

The company works with frontier AI teams to develop realistic, high-quality reinforcement learning environments that allow AI agents to perform increasingly complex, long-horizon tasks. Its focus includes computer and tool-use environments across financial services, including workflows such as financial modeling, presentation creation, quantitative analysis, and other specialized professional tasks.

The company is an early-stage startup with approximately 10 employees and $8.5M in funding. The engineering team is intentionally small and operates with a high-ownership, startup-oriented culture where engineers are expected to move quickly, make decisions independently, and take ownership across the product and technical stack.

This role is an opportunity to join the founding engineering team and help establish the platform, infrastructure, engineering practices, and technical culture from the ground up.

The role sits at the intersection of platform engineering, reinforcement learning, AI-agent evaluation, synthetic data generation, developer tooling, and customer-facing technical work. Engineers will build the infrastructure required to create and run RL environments at scale while also researching and developing increasingly realistic environments for frontier AI agents.

What You Will Do

RL Environment & Platform Engineering

• Build infrastructure for training and inference across reinforcement learning environments.
• Design and develop scalable platforms that support customer usage and large-scale AI-agent evaluation.
• Build systems that enable the creation, execution, monitoring, and management of complex RL environments.
• Improve the quality, reliability, and throughput of environment generation and execution.
• Develop platform capabilities that allow teams to efficiently create and operate increasingly sophisticated agent environments.
• Work across backend, infrastructure, and platform layers to support rapidly evolving research and product requirements.

AI Agents, RL & Evaluation

• Research and develop next-generation reinforcement learning environments for advanced AI agents.
• Build realistic, long-horizon environments that challenge frontier models with increasingly complex tasks.
• Develop evaluations, benchmarks, and environments for computer and tool-using agents.
• Build domain-specific environments and verifiers for financial services and other professional workflows.
• Design verifiable reward systems for tasks such as financial modeling, presentation generation, quantitative analysis, and other complex workflows.
• Experiment with new approaches to agent evaluation, training environments, and reinforcement learning.

Synthetic Data & Environment Creation

• Build software and tooling to dramatically increase the quality and throughput of RL environment creation.
• Develop synthetic data pipelines for generating realistic and challenging problems.
• Automate environment creation and validation wherever possible.
• Identify bottlenecks in environment development and create systems that improve efficiency.
• Develop analytics and infrastructure to measure environment costs, throughput, bottlenecks, and operational performance.
• Build systems for managing subject matter expert workflows and contributions.

Engineering Ownership & Team Building

• Establish engineering practices, development processes, and technical standards from the ground up.
• Own significant technical projects and drive them from concept through implementation and deployment.
• Work closely with customers, users, and subject matter experts to understand requirements and improve environments.
• Prioritize competing roadmap initiatives based on user impact, technical feasibility, and business needs.
• Move quickly in a highly iterative startup environment.
• Help shape the engineering organization, culture, and technical direction as the company grows.

Ideal Candidate Background

Experience Requirements

• 5–12 years of professional experience in platform engineering, full-stack engineering, ML infrastructure, or related technical roles.
• Strong software engineering fundamentals with experience building production systems.
• Experience working with reinforcement learning environments, AI-agent evaluations, ML infrastructure, or adjacent AI systems.
• Experience working at an early-stage startup, high-growth company, or similarly fast-moving technical environment.
• Experience owning technical projects end-to-end.
• Comfortable working across product, infrastructure, and research-oriented problems.
• Experience operating independently with significant ownership and limited bureaucracy.
• Strong ability to move quickly and iterate in an ambiguous environment.
• Former founder or early-stage startup experience is a strong plus.

Technical Requirements

• Strong Python experience.
• Strong TypeScript or comparable modern programming language experience.
• Experience building platform, backend, or full-stack systems.
• Familiarity with reinforcement learning, AI-agent evaluations, benchmarks, or ML infrastructure.
• Experience building scalable infrastructure for AI/ML systems.
• Experience with Docker and cloud infrastructure.
• Experience with AWS or comparable cloud platforms.
• Familiarity with LLM tooling and agent frameworks.
• Experience building APIs, services, and developer tooling.
• Strong understanding of production software engineering practices.
• Ability to design systems that support high-throughput workloads.
• Comfortable working with rapidly evolving AI technologies and technical requirements.

Product & Engineering Requirements

• Experience building products or infrastructure used by technical users, customers, researchers, or developers.
• Ability to prioritize across a large and evolving technical roadmap.
• Strong product and user ownership.
• Experience translating user or customer requirements into technical solutions.
• Comfortable working directly with customers, users, and subject matter experts.
• Ability to balance research experimentation with production engineering requirements.
• Experience building tools that improve engineering or data-generation workflows.
• Strong understanding of reliability, scalability, observability, and operational performance.
• Comfortable taking ownership of ambiguous problems without requiring detailed specifications.

Soft Skills

• Extremely high ownership and initiative.
• Comfortable moving quickly and iterating frequently.
• Strong curiosity about AI, reinforcement learning, and agentic systems.
• Excellent problem-solving ability.
• Strong written and verbal communication.
• Comfortable communicating directly with customers and subject matter experts.
• Highly adaptable and willing to work across different parts of the stack.
• Comfortable making decisions with incomplete information.
• Strong prioritization and project management skills.
• Collaborative and able to work effectively in a very small team.
• Comfortable helping establish processes and engineering culture from scratch.
• Willing to experiment, learn quickly, and adjust based on results.

Compensation & Benefits

• $200,000 – $250,000 base salary.
• 0.15% – 0.30% equity.
• Full-time position.
• Fully on-site in San Francisco, 5 days per week.
• Opportunity to join a very early-stage engineering team.
• Significant technical ownership and influence over engineering direction.
• Opportunity to work directly on frontier AI-agent training and evaluation infrastructure.
• Opportunity to work closely with customers, AI researchers, and subject matter experts.

Why Join

• Work on frontier AI-agent training and evaluation infrastructure.
• Build reinforcement learning environments for increasingly capable AI agents.
• Work at the intersection of platform engineering, RL, synthetic data, and AI-agent evaluation.
• Join an extremely small engineering team where individual contributions have significant impact.
• Help establish engineering practices and technical culture from the ground up.
• Own large technical projects across platform, infrastructure, and product.
• Work directly with advanced AI teams and customers.
• Build systems that improve the quality and throughput of RL environment creation by orders of magnitude.
• Work on challenging domain-specific problems across financial services and other professional workflows.
• Operate in a highly autonomous, fast-moving startup environment.
• Opportunity to shape both the technology and the engineering organization as the company grows.