Member of Technical Staff – Platform Engineering

Member of Technical Staff – Platform Engineering

Location: San Francisco, California, USA (in-office)

Salary: $200,000–$400,000 base + 0.1–0.5% equity

Experience: 3+ years

Positions available: 2

Working hours: On-site, 9-9-6 schedule

Start date: Immediate

About the Company

Join a fast-growing, seed-stage AI company based in San Francisco that is transforming how enterprises automate complex workflows and how AI models are evaluated.

The company embeds engineers within mid-market engineering and life sciences enterprises to automate real-world processes, develop evaluations for those automations, and turn the resulting data and reinforcement learning (RL) environments into valuable resources for leading AI research labs.

Already generating an eight-figure annualised revenue run rate, the company is growing rapidly and building the infrastructure needed to support the next generation of AI systems.

The Role

As a Member of Technical Staff specialising in Platform Engineering, you will take ownership of the core platform that powers the company's AI workflows, data pipelines and evaluation systems. You will build the infrastructure that enables AI agents to run complex tasks over extended periods across model APIs, remote environments and background services. You'll also develop the pipelines that execute tasks, process evaluation results and deliver datasets, alongside tools that allow domain experts to create and review tasks.

The long-term ambition is to scale processing to trillions of tokens per week. This requires robust distributed systems, high-performance infrastructure and excellent engineering standards. Joining at an early stage, you will have significant influence over the platform architecture, technical direction and infrastructure practices. Unlike other roles focused primarily on rapid delivery, this position places particular emphasis on building reliable, scalable systems the right way.

Key Responsibilities

• Design, build and maintain scalable platform infrastructure for AI-powered workflows.
• Develop distributed systems supporting long-running agent executions across model APIs, remote environments and background services.
• Build and optimise data pipelines for task execution, evaluation results and dataset delivery.
• Improve system performance, throughput, latency, reliability and infrastructure efficiency.
• Design robust mechanisms for failure recovery, observability, debugging and monitoring across services.
• Build internal tools that enable domain experts to create, manage and review tasks.
• Identify bottlenecks and develop solutions to support large-scale AI processing.
• Own technical projects from requirements and architecture through implementation, release and ongoing optimisation.
• Establish high engineering standards, architectural best practices and reliable development processes.
• Collaborate closely with the founding team and other engineers to shape the platform's long-term technical direction.

Requirements

• At least three years of relevant software engineering experience, with strong hands-on technical depth.
• Proven production experience in distributed systems, data pipelines, infrastructure engineering or performance optimisation.
• Experience building, operating or scaling production systems, with measurable results in areas such as throughput, latency, reliability, failure rates or cost.
• Strong understanding of system reliability, scalability, fault tolerance and observability.
• Experience diagnosing complex failures and improving the performance of distributed services.
• Demonstrated ownership of at least one significant project from initial requirements through to production release.
• Ability to solve ambiguous problems independently and make sound architectural decisions without relying on fully specified requirements.
• Experience working in a high-calibre engineering team with strong technical standards, or as an early infrastructure engineer who personally built and scaled a startup's infrastructure.
• Comfortable working in person in San Francisco, following a demanding 9-9-6 schedule.

Particularly relevant experience includes agent infrastructure, model inference, AI evaluation systems and large-scale data processing. Familiarity with technologies such as Kafka and Kubernetes is beneficial.

Candidates should demonstrate both strong engineering fundamentals and the ability to build reliable systems at scale. Experience at respected engineering organisations or on high-performing infrastructure teams is valuable, as is a proven record of independently scaling infrastructure in a rapidly growing startup.

What's on Offer?

• Competitive base salary of $200,000–$400,000.
• Equity participation of 0.1–0.5%.
• The opportunity to own and shape the company's core platform infrastructure.
• Significant influence over architecture, reliability standards and engineering best practices.
• The technical challenge of building infrastructure for large-scale AI agent execution and evaluation.
• Close collaboration with a small, highly technical team and the founding team.
• The opportunity to help scale a fast-growing company working with leading AI research labs.

Interview Process

1. Initial screening: Profile review by the recruitment team and the company.
2. Technical interview 1: One-hour whiteboard system-design interview.
3. Technical interview 2: One-hour open-ended coding challenge. AI agents are permitted, with an emphasis on problem-solving and technical judgement.
4. On-site assessment: A full day in the San Francisco office.
5. Offer: Successful candidates proceed to the offer stage.

Important Information

This role requires working in person in San Francisco, following a 9-9-6 schedule. The company is taking a considered approach to this hire and places a high priority on engineering quality, technical depth and demonstrated ownership.

Visa sponsorship is limited. Eligible profiles may include US citizens, green card holders, refugees and asylees, and candidates with qualifying H-1B, O-1 or TN status.

Interested in building the infrastructure that powers the next generation of AI systems? Apply now.