Senior Platform Engineer, Applied AI
About The Position
The company is building the foundational data and evaluation infrastructure powering the world's leading frontier AI labs. Its platform enables organizations to generate, manage, evaluate, and improve high-quality datasets that power the next generation of large language models and AI systems. By combining human-in-the-loop workflows, scalable distributed infrastructure, and modern AI tooling, the company has become critical infrastructure for some of the fastest-moving AI organizations in the world. Backed by Y Combinator, the company has scaled from $0 to over $100M ARR in just 14 months after raising a $30M Series A at a $300M valuation. As the business rapidly expands across multiple AI verticals, it is investing heavily in the core platform that powers every product and engineering team. As a Senior Platform Engineer, Applied AI, you'll own the shared infrastructure that enables AI data generation, evaluation pipelines, distributed compute, and developer productivity while partnering closely with engineering leadership to scale one of the fastest-growing AI infrastructure companies in the market. This is a rare opportunity to join an AI-native startup where engineers own foundational platform architecture, influence company-wide technical direction, and build infrastructure powering frontier AI development at massive scale.
Requirements
- 6–10 years of software engineering experience
- Experience building platform infrastructure or distributed backend systems
- Experience working at high-growth VC-backed startups or data infrastructure companies
- Experience owning core infrastructure with significant engineering impact
- Experience designing highly scalable production systems
- Experience building developer platforms or shared infrastructure services
- Experience mentoring engineers and influencing technical direction
- Strong startup ownership with demonstrated engineering leadership
- Strong backend engineering experience with Node.js and/or Python
- Deep experience with Kubernetes and cloud-native infrastructure
- Experience building distributed systems handling high-throughput workloads
- Strong understanding of AWS or GCP cloud architecture
- Experience with Kafka, Redis, Elasticsearch, or similar distributed technologies
- Strong knowledge of observability, reliability engineering, and production operations
- Experience building developer tooling, deployment systems, or internal platforms
- Experience designing highly available backend services and platform APIs
- Strong software engineering fundamentals across scalability, reliability, and distributed architecture
- Strong systems thinking
- High ownership mentality
- Excellent technical communication
- Comfortable operating with ambiguity
- Strong collaboration skills
- Startup mentality with high execution velocity
- Structured problem solver
- Engineering leadership mindset
- Passion for building scalable infrastructure
- Enjoys mentoring and elevating engineering teams
Nice To Haves
- Experience supporting AI infrastructure, data infrastructure, or large-scale compute systems preferred
- Bachelor's degree in Computer Science or another technical discipline from a top engineering program preferred
- Strong academic background from a Top-30 Computer Science university (US, Canada, or Europe) preferred
- Exceptional candidates with outstanding infrastructure engineering experience considered
Responsibilities
- Design and build the core platform infrastructure powering AI data generation and evaluation systems
- Architect highly scalable distributed systems supporting high-throughput AI workloads
- Build shared platform services used across multiple engineering teams and AI products
- Design cloud-native infrastructure across Kubernetes, distributed compute, storage, and networking
- Improve platform scalability, observability, reliability, fault tolerance, and performance
- Build internal developer platforms, deployment tooling, and engineering infrastructure
- Develop backend services using Node.js and Python
- Design distributed messaging, caching, and data processing systems using Kafka, Redis, and Elasticsearch
- Partner closely with product engineering teams to accelerate feature development through shared infrastructure
- Lead architecture decisions around platform scalability, cloud infrastructure, and distributed systems
- Mentor engineers while establishing engineering best practices across platform architecture
- Continuously improve deployment automation, monitoring, developer productivity, and operational excellence
Benefits
- Competitive Equity Package
- 5 Days/Week On-site in San Francisco
- Opportunity to build foundational infrastructure for frontier AI companies
- Direct collaboration with founders and engineering leadership
- Significant ownership over platform architecture and technical direction
- High-impact engineering culture with exceptional career growth
- Fast-growing AI infrastructure company with proven product-market fit
- Opportunity to shape the future of AI data infrastructure
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What This Job Offers
Job Type
Full-time
Career Level
Senior
