Member of Technical Staff [Platform]

NeoCognition; Palo Alto (CA); On-site; USD 180,000 - 240,000; Full time

Job summary

NeoCognition is seeking a Member of Technical Staff to design and build the internal systems that power our research and product deployments. You’ll craft the tooling, infrastructure, and developer experience to accelerate experimentation and scale operations.

This role sits at the intersection of infrastructure engineering, developer tooling, and MLOps. You’ll work with researchers and software engineers to ensure robust, reproducible data, models, and workflows across environments.

Qualifications

• Strong software engineering background in infrastructure, platform, or DevOps.
• Proficient with cloud environments (AWS, GCP) and container orchestration (Docker, Kubernetes).
• Experience building internal developer tools, automation frameworks, or internal platforms.
• Familiar with data pipelines, job scheduling, or ML experimentation workflows.
• Excellent problem-solving skills and a track record of improving developer velocity or system reliability.
• Strong communication skills and ability to collaborate with research and product teams.

Responsibilities

• Design, build, and maintain the core infrastructure supporting research and product environments (cloud compute, storage, CI/CD pipelines, observability, security).
• Develop and manage internal developer tools, automation systems, and shared libraries that accelerate productivity across teams.
• Build reliable systems for data management, model experimentation, evaluation, and deployment in both research and production contexts.
• Define and evolve our build, test, and release workflows, ensuring seamless integration from prototype to production.
• Implement and monitor observability and performance metrics across services and pipelines.
• Collaborate with research and product teams to diagnose bottlenecks and optimize workflows for reproducibility.
• Contribute to infrastructure strategy and architecture, helping scale as the company grows.

Skills

Infrastructure engineering

Platform engineering

DevOps engineering

Software engineering

Tools

Docker

Kubernetes

GitHub Actions

Terraform

CI/CD

Job description

About the Role

As a Member of Technical Staff [Platform] at NeoCognition, you’ll design and build the internal systems that power everything we do — from research experiments to product deployments.

You’ll create the tooling, infrastructure, and developer experience that enable our team to iterate rapidly, deploy confidently, and scale intelligently.

This role sits at the intersection of infrastructure engineering, developer tooling, and MLOps. You’ll collaborate closely with research scientists and software engineers to ensure that our data, model, and product workflows are robust, reproducible, and efficient.

Responsibilities

• Design, build, and maintain the core infrastructure supporting our research and product environments (cloud compute, storage, CI/CD pipelines, observability, security).

• Develop and manage internal developer tools, automation systems, and shared libraries that accelerate productivity across teams.

• Build reliable systems for data management, model experimentation, evaluation, and deployment in both research and production contexts.

• Define and evolve our build, test, and release workflows, ensuring seamless integration from prototype to production.

• Implement and monitor observability and performance metrics across services and pipelines.

• Collaborate closely with research and product teams to diagnose bottlenecks, optimize workflows, and ensure reproducibility of results.

• Contribute to infrastructure strategy and architecture, helping shape how we scale as the company grows.

Qualifications

Required:

• Strong software engineering background with experience in infrastructure, platform, or DevOps engineering.

• Proficiency with cloud environments (AWS, GCP, or similar), containerization/orchestration (Docker, Kubernetes), and CI/CD systems (GitHub Actions, Terraform, etc.).

• Experience building and maintaining developer tools, automation frameworks, or internal platforms.

• Familiarity with data pipelines, job scheduling, or ML experimentation workflows.

• Excellent problem-solving skills and a track record of improving developer velocity or system reliability.

• Strong communication skills and ability to collaborate with both research and product engineering teams.

Nice to have:

• Experience with machine learning infrastructure, training pipelines, or model evaluation tooling.

• Background in monitoring and observability (e.g., Prometheus, Grafana, Datadog).

• Knowledge of infrastructure as code and configuration management best practices.

• Interest in designing systems that support reproducible, safe, and efficient AI development.

• Prior experience in an early-stage startup or small research organization.