Member of Technical Staff [Platform]

Recruiting From Scratch • Palo Alto, CA

5d • Onsite

About The Position

The Member of Technical Staff on the Platform team will build backend infrastructure and systems supporting the company’s research and production deployments. This is a high-ownership, hands-on role for a senior- or staff-level individual contributor who writes production code daily and owns complete systems, from architecture and implementation through deployment and ongoing improvement.

The role focuses on building a customer-facing platform from scratch for AI agent products. It spans backend services, distributed systems, data storage, cloud infrastructure, developer tooling, and production LLM or agent workloads. The engineer will help move experimental agent systems from research prototypes into reliable production deployments, while balancing rapid iteration with security, performance, scalability, reliability, and maintainability.

The position works closely with research scientists and product engineers on agent harnesses, memory, orchestration, model training, data collection, and evaluations. The company is a research-driven AI startup building agents that can learn and adapt to different environments. Its approximately 15-person team includes researchers and founders with backgrounds at Meta, DeepMind, and Microsoft. The role is fully onsite in Palo Alto, California, five days per week. Base salary is $200,000–$300,000, with competitive equity. H-1B and OPT transfers are supported.

Requirements

• Typically 5–12 years of experience as a Backend or Platform Engineer.
• Experience building a product platform from scratch for an external, customer-facing product; identified as a dealbreaker.
• A combination of experience in a mature technical organization and an early-stage startup or research environment; identified as a baseline requirement.
• Experience building or scaling production backend systems and customer-facing products, including service hosting, data storage, availability, and scaling.
• Currently operating at a senior or staff-level individual-contributor level, with daily hands-on coding and ownership of complete systems.
• A Computer Science or other highly technical STEM degree from a top-20 university; identified as a dealbreaker.
• Strong programming, algorithms, systems, and software architecture fundamentals.
• Proficiency in Python or Go for backend services.
• Experience designing and building APIs, databases, and data storage systems.
• Understanding of distributed systems, service architecture, concurrency, and core programming concepts.
• Working-depth knowledge of Kubernetes and experience with containerized deployment.
• Experience with at least one major cloud platform, such as AWS or GCP.
• Hands-on production experience with LLM or agent workloads; identified as a dealbreaker.
• Experience in at least one of agent serving, orchestration, memory, tool use, or evaluations.
• Ability to design for availability, security, networking, reliability, performance, and scaling.
• Experience taking backend systems from initial design through production operation, rather than only maintaining existing infrastructure.
• Strong technical judgment, independent problem-solving, ownership, and accountability.
• Ability to collaborate and communicate effectively across research and engineering disciplines.
• Comfort operating in an early-stage, fast-moving environment with ambiguity, evolving requirements, and broad ownership.
• Ability to balance rapid experimentation with reliability and maintainability, learn quickly, and prioritize without sacrificing engineering quality.

Nice To Haves

• Experience launching or scaling product infrastructure from scratch to significant scale.
• Familiarity with data pipelines and job scheduling.
• Experience with machine-learning experimentation workflows.
• Experience with LLM serving and agent orchestration, memory systems, tool use, or agent evaluations.
• Experience with Kubernetes and AWS or GCP.
• Experience with Postgres, object storage, and vector stores.
• Experience at the intersection of infrastructure engineering, developer tooling, and MLOps.
• Experience supporting research teams and production AI systems.
• Contributions to open-source projects or technical research.
• Active participation in AI/ML communities or relevant technical research.
• Experience with AI-native tools or workflows.
• A strong growth trajectory in technically demanding environments.

Responsibilities

• Design, build, and maintain backend platform services for external, customer-facing AI products and LLM-powered agents.
• Own key aspects of platform availability, networking, security, scaling, and service reliability.
• Design and implement APIs, backend systems, databases, storage, and data layers for production agent workloads.
• Take platform systems from architecture and implementation through deployment, operation, and continued improvement.
• Build scalable infrastructure that supports increasing workloads and customer demand, and move agent systems from research prototypes into production.
• Develop and maintain infrastructure for research and product environments, including internal developer tools, automation, and shared libraries.
• Design and evolve CI/CD pipelines and build, test, and release workflows; improve developer productivity and experience.
• Automate repetitive infrastructure and development tasks, improve reproducibility, and establish reusable platform standards and workflows.
• Build and maintain production systems for agent serving, orchestration, memory, tool use, and related runtime infrastructure.
• Collaborate with product engineers and researchers to support evolving agent architectures and transition experimental systems into robust services.
• Design and maintain data storage and infrastructure for research, model experimentation, evaluation, and deployment, including databases, object storage, and vector stores as needed.
• Improve data and experiment workflows, remove research infrastructure bottlenecks, and help move successful experiments toward production.
• Implement observability, monitor performance, reliability, and availability, diagnose bottlenecks, and improve system performance and operational reliability.
• Establish practices and mechanisms for monitoring, debugging, failure resolution, and dependable production operation.
• Help ensure infrastructure is secure, scalable, and cost-efficient, and proactively identify technical and operational risks.
• Translate research and product requirements into reliable platform capabilities; contribute to architecture decisions and infrastructure strategy.
• Balance rapid experimentation with production requirements and communicate technical trade-offs across research and engineering disciplines.
• Own major platform systems, make architecture and implementation decisions, write code daily, and help establish engineering standards as the company grows.

Benefits

• Competitive equity package.
• Opportunity to work on AI agent systems designed to learn and adapt to different environments.
• Opportunity to build the backend platform powering research and customer-facing products.
• High ownership over infrastructure, backend architecture, and production AI systems.
• Opportunity to shape engineering practices and platform architecture at an early-stage company.
• Work on technically challenging problems spanning distributed systems, agent serving, and AI infrastructure.
• Work alongside a founding team and researchers with backgrounds at Meta, DeepMind, and Microsoft.
• The company has raised $40 million in seed funding and recently emerged from stealth.
• H-1B and OPT transfers are supported.

What This Job Offers

• Job Type: Full-time
• Career Level: Senior