Member of Technical Staff — Platform

Palo Alto, CA · On-site, 5 days/week · Full-time

$200K–$300K base + competitive equity

This is an AI agent lab focused on specialized intelligence.

The core thesis is that the future is not one general-purpose super-agent. It is a set of specialized agents that can learn continuously inside real workflows and become dependable at specific tasks.

The founding team’s research has already helped shape the modern agent ecosystem, and that work is used in frontier models from OpenAI, Anthropic, Google, and others.

The company recently came out of stealth with a $40M seed round backed by Cambium Capital, Walden Catalyst Ventures, Vista Equity Partners, Intel CEO Lip-Bu Tan, and Databricks co-founder Ion Stoica.

The team includes people from Meta, DeepMind, and Microsoft, and the customer base is enterprises and established SaaS companies building or embedding agents into real products.

This is a Member of Technical Staff role focused on platform: the infrastructure, tooling, and serving layer that let the team run agents reliably in research and production.

You will work at the boundary between infrastructure engineering, developer experience, and MLOps, with close collaboration from research scientists and software engineers.

The work is not a narrow DevOps function. You will be building the systems that determine whether experiments are reproducible, deployments are safe, and agent workloads can be observed and scaled without guesswork.

The broader engineering surface area has three buckets:

• Agent harness: the product-facing agent system and its memory.

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• Platform: hosting, availability, scaling, security, networking, and serving infrastructure.
• Research: training, data collection, and evaluations.

This role sits primarily in the platform bucket, while staying close enough to the harness and research workflows to remove friction where the systems meet.

Agent infrastructure fails in ways that ordinary application infrastructure does not.

State is long-lived, behavior is stochastic, evaluation is noisy, and one bad deployment can contaminate experiments across the stack.

The platform has to support fast iteration without losing control of versioning, reproducibility, observability, access control, rollout safety, and data lineage.

The hard part is not simply running services in the cloud.

The hard part is creating the operating layer that lets researchers and engineers move quickly while still knowing exactly what ran, what changed, and whether the result can be trusted.

• Core platform infrastructure: cloud compute, storage, deployment systems, and the internal primitives that the rest of the team depends on.
• Agent serving layer: the systems that host and operate agents in production, including availability, scaling, networking, and rollout control.
• Developer tooling and automation: internal tools, shared libraries, and workflows that reduce friction across research and engineering.
• Experiment and evaluation infrastructure: reliable systems for running experiments, tracking outcomes, comparing versions, and promoting model or agent changes with discipline.
• Build, test, and release workflows: pipelines that let prototypes move into production without creating ad hoc release paths.
• Observability: logs, metrics, traces, dashboards, and alerting that make platform and agent failures diagnosable quickly.

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• Security and access boundaries: the controls that protect internal systems and production workloads as the surface area grows.
• Architecture: contribute to the decisions that define how the platform scales as usage and model complexity increase.

You are likely a fit if you have:

• 2–7 years of experience in infrastructure, platform engineering, DevOps, or related systems work, with real production ownership.
• Built systems that other engineers rely on every day, not just internal scripts or one-off automations.
• Experience scaling production workloads where reliability, release discipline, and debugging speed mattered.
• Strong judgment around compute, storage, deployment, and operational tradeoffs.
• Built or operated tooling around experimentation, evaluation, CI/CD, observability, or release safety.
• Comfort working in a small team where the best answer is often a design choice rather than a process change.
• Enough technical depth to discuss failures in agent or ML systems without hand-waving through the hard parts.
• The ability to work directly with researchers and product engineers and translate ambiguous needs into infrastructure that holds up in practice.

The company is early enough that the platform is still being defined, but far enough along that the systems already need to be production-grade.

That combination creates a specific kind of work: the architecture choices you make now will influence how agents are evaluated, deployed, observed, and improved for years.

This is a good seat for someone who wants ownership of the boring parts that are actually the product constraints: state, reliability, rollout safety, data integrity, and the mechanics of turning research into something dependable.

• You want a narrow infrastructure lane with no exposure to research or product needs.
• You prefer to work from fully specified tickets rather than open technical problems.
• You are uncomfortable owning production reliability and debugging failures end to end.
• You want a role where the infrastructure is already settled and the work is mostly maintenance.
• You are not interested in systems where evaluation, deployment, and runtime behavior are tightly coupled.

• Base salary: $200K–$300K
• Equity: competitive
• Location: Palo Alto, CA
• Work model: in-person, 5 days per week
• Visa sponsorship: available
• Employment: full-time

Typical process: initial screen, systems deep-dive, technical conversation on platform design and production failures, then a team session.

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