ML Infrastructure Engineer

Навыки

• AWS
• Azure
• Многопоточность
• C++
• Отладка и поиск ошибок
• Распределённые системы
• Docker

О компании и продукте

• We're a seed-stage enterprise AI infrastructure company building the context layer that makes AI agents reliable, accurate, and secure for critical business operations — including highly regulated industries like insurance, banking, asset management, and healthcare. Our platform automatically constructs a governed, real-time domain model across all enterprise data, enabling AI agents to make confident, auditable decisions in production.
• As an ML Infrastructure Engineer, you'll own the systems that keep our agents running reliably and fast at scale. This is a hands-on production engineering role — focused on real-world impact, not research. You'll design, build, and scale our inference and model-serving infrastructure as concurrency and customer demands grow.

Задачи

• Own inference and model-serving infrastructure end to end — from initial design through production deployment and ongoing scaling
• Build and scale systems that enable AI agents to run reliably and efficiently under high and increasing concurrency
• Identify and resolve infrastructure bottlenecks in collaboration with ML and platform engineering teams
• Optimize systems for latency, throughput, and reliability across cloud-hosted production environments
• Drive observability, monitoring, and debugging practices across our production ML stack

Требования

• Dealbreakers — all required
• 5+ years of hands-on experience building and operating machine learning inference systems, model-serving platforms, or ML infrastructure in production environments
• Demonstrated experience designing and scaling inference-serving infrastructure using tools such as TensorFlow Serving, TorchServe, Triton, KServe, or equivalent custom systems
• Proven ability to optimize production ML systems for latency, throughput, and reliability at scale
• Experience with containerization and orchestration (Docker, Kubernetes) for deploying and scaling ML workloads
• Background in distributed systems that handle high concurrency and dynamic resource allocation under load
• Proficiency with monitoring and observability tooling — e.g., Prometheus, Grafana, ELK stack, distributed tracing
• Experience deploying and managing ML systems on cloud platforms (AWS, GCP, or Azure)
• Proficiency in at least one systems or backend language: Python, Go, Rust, C++, or Java

Будет плюсом

• Experience with knowledge graphs, semantic search, or graph databases (e.g., Neo4j, Amazon Neptune, or similar)
• Background in real-time inference or low-latency serving requirements
• Familiarity with agentic AI systems, autonomous agents, or multi-step reasoning pipelines
• Experience with enterprise data infrastructure, data pipelines, or data integration platforms
• LOCATION & WORK ARRANGEMENT
• This is a full-time, on-site role based in San Mateo, CA
• On-site collaboration is an important part of how this small, fast-moving team operates
• Visa sponsorship is not available for this position
• WHY JOIN
• Early-stage opportunity with significant ownership and impact — you'll shape foundational infrastructure decisions
• Work on genuinely hard distributed systems problems in a production AI context
• Small, experienced team with deep ML and enterprise engineering backgrounds
• Well-funded at the seed stage with strong institutional backing and a clear enterprise customer focus

Паспорт вакансии

История публикации

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