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

1. 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.

2. 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.

3. 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.

4. 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.

5. 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.

6. 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.

7. 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.

• Identify opportunities to improve the platform, developer experience, and infrastructure architecture.

• Help establish the technical foundations and engineering standards for the company's next stage of growth.

Ideal Candidate Background

Experience 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 — dealbreaker.

• Ideally, experience building backend platforms for LLM or agent products.

• A combination of Big Tech and early-stage startup or research-environment experience — baseline requirement.

• Experience at a mature technical organization with strong engineering practices and code quality.

• Experience in a startup or research environment involving ambiguity, rapid iteration, and broad ownership.

• Currently operating at a senior or staff-level individual contributor level.

• Still writes code daily and takes ownership of complete systems rather than individual components.

• Experience building or scaling production backend systems.

• Experience working with external customer-facing products, including service hosting, data storage, availability, and scaling.

• Strong trajectory of growth, increasing responsibility, and technical contribution.

Education Requirements

• Computer Science or another highly technical STEM degree from a top-20 university — dealbreaker.

• Strong fundamentals in programming, algorithms, systems, and software architecture.

• Demonstrated technical depth through production engineering work, research, or innovative projects.

• Active participation in AI/ML communities, technical research, or relevant open-source work is beneficial.

Technical Requirements

• Strong backend engineering fundamentals.

• Proficiency in Python or Go for backend services.

• Experience designing and building APIs.

• Experience with databases and data storage systems.

• Understanding of distributed systems and service architecture.

• Working-depth knowledge of Kubernetes.

• Experience with at least one major cloud platform, such as AWS or GCP.

• Hands-on production experience with LLM or agent workloads.

• Experience with agent serving, orchestration, memory, tool use, or evaluations.

• Strong understanding of concurrency, system architecture, and core programming concepts.

• Ability to design systems for availability, security, networking, reliability, and scaling.

• Experience building backend platforms from scratch rather than only maintaining existing infrastructure.

• Ability to write production-quality code and own systems end-to-end.

Backend Platform Requirements

• Experience building a backend platform for an external, customer-facing product.

• Experience with service hosting and backend deployment.

• Understanding of service availability and production reliability.

• Experience designing scalable backend services and APIs.

• Experience with databases, Postgres, object stores, or vector stores.

• Understanding of distributed systems and data storage.

• Experience operating services in a major cloud environment.

• Working-depth knowledge of Kubernetes and containerized deployment.

• Ability to reason about security, networking, scaling, and performance.

• Experience taking backend systems from initial design through production operation.

• Ability to support customer-facing LLM or agent workloads.

AI-Native Engineering Requirements

• Hands-on production work with LLM or agent workloads — dealbreaker.

• Experience in at least one of the following areas: agent serving, orchestration, memory, tool use, or evaluations.

• Genuine interest and technical engagement in AI-native systems.

• Strong engineering fundamentals beyond simply integrating AI APIs.

• Ability to understand the infrastructure requirements of AI agent products.

• Experience building systems that support the deployment and operation of AI-powered applications.

• Comfort collaborating with research teams and adapting infrastructure to evolving AI systems.

• Ability to distinguish foundational backend engineering problems from model research problems.

• Experience with AI-native tools or workflows is beneficial.

Big Tech & Startup Experience Requirements

• Experience in Big Tech or another mature engineering organization.

• Experience in an early-stage startup or research environment.

• Ability to apply mature engineering practices while remaining effective in ambiguous environments.

• Comfortable building systems from scratch without relying on established processes.

• Able to work across multiple technical areas and take broad ownership.

• Experience navigating evolving product requirements and research-driven development.

• Strong ability to balance rapid experimentation with reliability and maintainability.

• Experience launching or scaling product infrastructure from scratch is a plus.

Soft Skills

• Strong technical judgment and independent problem-solving.

• High ownership and accountability.

• Ability to work effectively across research and engineering disciplines.

• Clear communication and collaboration.

• Comfortable operating in an early-stage, fast-moving environment.

• Proactive approach to technical challenges and new technologies.

• Ability to learn quickly and adapt to evolving requirements.

• Strong sense of responsibility for system reliability and product outcomes.

• Willingness to challenge existing approaches and develop new solutions.

• Ability to prioritize effectively without sacrificing engineering quality.

Nice-to-Have Qualifications

• Experience launching or scaling product infrastructure from scratch to significant scale.

• Familiarity with data pipelines and job scheduling.

• Experience with ML experimentation workflows.

• Experience with LLM serving and agent orchestration.

• Experience with memory systems, tool use, or agent evaluations.

• Experience with Kubernetes and AWS or GCP.

• Experience with Postgres, object storage, and vector stores.

• Experience working at the intersection of infrastructure engineering, developer tooling, and MLOps.

• Experience supporting research teams and production AI systems.

• Experience contributing to open-source projects or technical research.

• Strong growth trajectory in technically demanding environments.

Compensation & Benefits

• Base salary: $200,000–$300,000.

• Equity: Competitive equity package.

• Work model: Fully onsite in Palo Alto, CA, 5 days per week.

• Employment: Full-time.

• Visa: H-1B and OPT transfers supported.

• Opportunity to join a research-driven AI company building next-generation agent systems.

• Work alongside a founding team and researchers with backgrounds at Meta, DeepMind, and Microsoft.

• Opportunity to build the backend platform powering both 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.

• Work on technically challenging problems spanning distributed systems, agent serving, and AI infrastructure.

Why Join

• Work on a hard, unsolved AI problem: Help build agent systems designed to learn and adapt to different environments.

• Join a research-driven team: Work alongside researchers and engineers with backgrounds at Meta, DeepMind, and Microsoft.

• Build the platform from the ground up: Take ownership of the backend infrastructure powering research and product deployments.

• Work directly with AI agent workloads: Build production systems for serving, orchestration, memory, tool use, and evaluations.

• Shape technical architecture: Help establish the infrastructure and engineering practices that will support the company's growth.

• Own complete systems: Work as a hands-on senior or staff-level engineer with broad technical responsibility.

• Combine mature engineering with startup speed: Apply strong engineering practices while building in a fast-moving, ambiguous environment.

• Join early with meaningful upside: The company has recently emerged from stealth with $40 million in seed funding.