Forward Deployed Engineer

zaimler • San Mateo, CA

2d • Hybrid

About The Position

We’re looking for a Forward-Deployed Engineer to join our founding team and bring our AI-native data infrastructure to life in real-world environments. This is a hybrid role blending infrastructure, product, and delivery—you’ll work across systems, tools, and teams to bring new deployments from concept to scale. Think of it as a "jack-of-all-systems" role: one foot in Kubernetes and Ray clusters, the other in semantic pipelines and user-facing use cases. You’ll be a first responder, architect, and operator—deploying zaimler’s semantic infrastructure into dynamic enterprise settings, collaborating closely with ML and infra engineers, and making sure customers get value from day one.

This role prefers candidates in or aligned to the Pacific Time Zone (PST) & will require travel onsite to customer locations.

Requirements

• 3+ years of experience in backend or infrastructure roles (infra/platform, SRE, or devops welcome)
• Deep familiarity with Kubernetes, Docker, Terraform, and Helm
• Experience managing or deploying distributed compute frameworks (Ray, Dask, Spark, etc.)
• Fluency in at least one scripting language (Python, Bash, Go preferred)
• Strong debugging skills across the stack—from network hiccups to memory leaks
• Experience deploying in cloud-native environments (AWS/GCP/Azure)
• Comfortable being customer-facing and managing ambiguity in real-time
• Strong sense of ownership and bias toward action in early-stage environments

Nice To Haves

• Experience deploying or integrating LLMs or vector databases
• Background in ML Ops or building/operating data infrastructure at scale
• Familiarity with GPU optimization, multi-tenancy, or air-gapped environments
• Prior startup or zero-to-one deployment experience
• Familiar with Ray Serve, VLLM, or similar frameworks
• You’ve been in a forward-deployed, solutions engineer or field engineer role before

Responsibilities

• Build, deploy, and scale zaimler’s platform in diverse customer environments (Kubernetes-native, often hybrid cloud)
• Own the infrastructure stack: provisioning, scaling, monitoring, and incident handling for data/ML pipelines.
• Stand up and optimize distributed compute systems using Ray, Kubernetes, and supporting cloud services.
• Bridge ML and infra. Help ML engineers productionize knowledge extraction, entity linking, and retrieval pipelines
• Write Terraform, Helm charts, Dockerfiles, and bash scripts that others rely on
• Contribute to internal tooling, automation, and observability to make zaimler repeatable and scalable.
• Interface directly with early customers to understand their environment, workflows, and edge cases.

What This Job Offers

• Job Type: Full-time
• Career Level: Mid Level
• Education Level: No Education Listed