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.
Full description
Who is Recruiting from Scratch:
Recruiting from Scratch is a premier talent firm that focuses on placing the best product managers, software, and hardware talent at innovative companies. Our team is 100% remote and we work with teams across the United States to help them hire.
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.
Member of Technical Staff [Platform] at recruitingfromscratch (Palo Alto, CA, Onsite) — aijobsearch
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.