Member of Technical Staff Platform Engineering

San Francisco CA

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

Early Stage $8.5M raised

On-site

$200000 $250000 Base 0.15% 0.30% Equity

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

No significant travel requirement specified.

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.

• 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.

• 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.

• 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.

• 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.

• 512 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.

• 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.

• 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.

• 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.

• $200000 $250000 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.

• 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.