About zaimlerzaimler is building the next-generation semantic platform that links fragmented enterprise data and extracts meaning with knowledge-distilled models. We're creating the foundation for AI systems that don't just generate, but retrieve, link, and reason over enterprise knowledge.
In just over a year, we've begun partnering with Fortune 500 design partners in insurance, travel, and technology, deploying our semantic platform into some of the world's most complex and high-volume data ecosystems. Our platform enables enterprises to make their data AI-ready from the start: automating ontology creation, data mapping, and retrieval-augmented reasoning at scale.
Our team comes from LinkedIn, Visa, Meta, and Branch, and has spent decades solving data and infrastructure challenges at scale. Backed by top VCs, we're building the next foundational layer for enterprise AI.
About the JobWe'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 locationsWhat You Will Be Doing- 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.
Prior Experience- 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
We may use artificial intelligence (AI) tools to support parts of the hiring process, such as reviewing applications, analyzing resumes, or assessing responses. These tools assist our recruitment team but do not replace human judgment. Final hiring decisions are ultimately made by humans. If you would like more information about how your data is processed, please contact us.
