About this role
Member of Technical Staff — Platform
Location
Palo Alto, CA
Fully onsite — 5 days per week.
Company Stage of Funding
Seed
The company has raised $40 million in seed funding, co-led by Cambium Capital and Walden Catalyst Ventures, with participation from Vista Equity Partners, Intel CEO Lip-Bu Tan, and Databricks co-founder Ion Stoica.
Office Type
On-site — 5 days per week
Salary
$200,000–$300,000 Base
Equity
Competitive Equity
Visa
Open to visa transfers, including H-1B and OPT transfers.
Employment Type
Full-time
Company Description
This research-driven AI company is building a new generation of AI agents that can continuously learn and adapt to different environments. Its technology is designed to help agents develop a model of their environment, learn domain-specific rules, and become more reliable at completing complex tasks.
The company recently emerged from stealth after raising $40 million in seed funding from leading investors, including Cambium Capital, Walden Catalyst Ventures, Vista Equity Partners, Intel CEO Lip-Bu Tan, and Databricks co-founder Ion Stoica.
The founding team and early researchers have backgrounds at Meta, DeepMind, and Microsoft. The team consists of approximately 15 people, many of whom hold PhDs, and is focused on advancing foundational AI research and agent systems.
The company is building a horizontal AI platform that can help enterprises and established SaaS companies develop agent workers or enhance existing products. Its work spans three connected areas: the agent harness and memory system, the backend serving platform that enables reliable deployment at scale, and the research systems supporting model training, data collection, and evaluation.
The company is looking for a Member of Technical Staff on the Platform team to build the backend infrastructure and systems that power research and production deployments. This is a high-ownership engineering role for someone who can build a customer-facing platform from scratch, work hands-on with LLM or agent workloads, and take ownership of an entire system rather than a narrow component.
The ideal candidate combines strong backend engineering fundamentals with experience across both Big Tech and early-stage startups or research environments. They should be a senior or staff-level individual contributor who still writes code daily and is comfortable working across backend services, distributed systems, data storage, cloud infrastructure, and AI-native workloads.
What You Will Do
- Build & Scale the Core Backend Platform
Design, build, and maintain the backend platform that powers external, customer-facing AI products.
Build backend services that host and serve LLM-powered agents reliably.
Own key aspects of availability, networking, security, scaling, and service reliability.
Design and implement APIs and backend systems that support agent workloads in production.
Build and maintain databases, storage systems, and data layers that support reliable product operation.
Develop scalable infrastructure that can support increasing workloads and customer demand.
Take ownership of entire systems, from architecture and implementation through deployment and ongoing improvement.
Make pragmatic engineering decisions that balance reliability, performance, scalability, and maintainability.
Build the platform foundations required to take AI agent systems from research prototypes to production deployments.
- Develop Infrastructure & Developer Tooling
Build and maintain core infrastructure supporting research and product environments.
Develop internal developer tools, automation systems, and shared libraries that improve engineering productivity.
Design and evolve CI/CD pipelines and build, test, and release workflows.
Improve the developer experience so engineers can iterate quickly and deploy confidently.
Build systems that make research and product workflows more reproducible and efficient.
Automate repetitive infrastructure and development tasks.
Establish reliable tooling and workflows that support rapid experimentation without compromising production quality.
Identify opportunities to simplify engineering processes and reduce operational overhead.
Help establish platform standards and reusable infrastructure as the company grows.
- Support LLM & Agent Workloads in Production
Build and maintain production systems for serving LLM-powered agents.
Work with agent orchestration, memory, tool use, and related runtime infrastructure.
Develop reliable backend services that support agent execution and availability.
Help design systems that allow agent workloads to operate securely and scale across different environments.
Collaborate with product engineers working on agent harnesses, memory systems, and agent capabilities.
Support the transition of experimental agent systems into robust production services.
Investigate and resolve issues involving agent serving, backend performance, and system reliability.
Build the platform capabilities needed to support increasingly sophisticated AI agent deployments.
Work closely with the research team to ensure that backend systems can support evolving agent architectures and workloads.
- Build Reliable Data, Storage & Experimentation Systems
Design and maintain data storage systems for research and production workflows.
Build reliable infrastructure for data management, model experimentation, evaluation, and deployment.
Work with databases, object storage, and vector stores as needed for platform workloads.
Improve data and experiment workflows to make results more reproducible.
Collaborate with research scientists to understand infrastructure needs and remove technical bottlenecks.
Support model evaluation and experimentation pipelines through robust backend systems.
Develop infrastructure that enables researchers to test ideas and move successful experiments toward production.
Improve the reliability, efficiency, and accessibility of shared data and compute systems.
Help create a consistent path from research experimentation to customer-facing deployment.
- Improve Reliability, Observability & Performance
Implement and maintain observability across backend services and infrastructure.
Define and monitor performance, reliability, and availability metrics.
Diagnose bottlenecks across services, pipelines, and infrastructure workflows.
Improve system performance and operational reliability.
Develop mechanisms to identify failures and resolve issues efficiently.
Establish practices for monitoring, debugging, and maintaining production systems.
Help ensure that infrastructure is secure, scalable, and cost-efficient.
Build systems that support dependable deployment and ongoing operation of AI agent products.
Proactively identify risks and technical improvements before they become operational problems.
- Collaborate With Research & Product Teams
Work closely with research scientists and software engineers to understand technical requirements.
Collaborate with product engineers building agent systems, memory, and orchestration capabilities.
Partner with researchers working on model training, data collection, and evaluations.
Translate research and product requirements into reliable platform capabilities.
Help diagnose bottlenecks and improve workflows across research and product development.
Contribute to architecture decisions and the company's infrastructure strategy.
Balance the need for rapid experimentation with the requirements of production systems.
Communicate technical trade-offs clearly and work collaboratively across disciplines.
Help shape how the company scales its engineering platform as the team and customer base grow.
- Take End-to-End Technical Ownership
Own major platform systems rather than working exclusively on isolated components.
Make architecture and implementation decisions independently while collaborating with the broader team.
Write production code daily and remain hands-on with complex technical problems.
Operate effectively in an early-stage environment with ambiguity and evolving requirements.
Build systems from scratch and scale them as product and research needs mature.
Bring engineering practices from mature technical organizations while maintaining the speed and flexibility of a startup.
Iden