Build and own end-to-end product surfaces: React/TypeScript frontends, APIs, and backend services in Python/Go. Ship features, work with customers, design APIs/data models, operate cloud deployments, and iterate on production systems to make AI infrastructure legible and fast.
About zaimler
AI agents can't reason over data they don't understand. Enterprise data today is fragmented across dozens of systems with no shared context, meaning, or structure, and that's why most enterprise AI is failing. The shift from copilots to autonomous agents is creating an entirely new infrastructure layer, and we're building it.
zaimler is the context infrastructure for the agentic era: a platform that automatically discovers domain knowledge, maps relationships, and gives AI agents the semantic understanding to operate with precision at scale. Imagine knowledge graphs that support real-time inference, built for systems that need to reason, not just retrieve.
zaimler was founded by Biswajit Das (ex-VP Engineering, Truera), a Data Infra veteran and former Chief Architect at Visa, and Sofus Macskassy (ex-Director of Engineering, LinkedIn), who built one of the largest knowledge graphs in production in the industry at LinkedIn. We're growing and deploying with major enterprises across insurance, travel, and technology. If you want to build infrastructure that the next decade of enterprise AI runs on, we'd love to talk.
Why this role exists
Our platform is used by engineers, and engineers judge infrastructure by how fast it stops confusing them.
We have a hard system underneath: graphs, real-time inference, agent behavior in production. Today, understanding it takes a conversation with us. That's a ceiling on how fast we grow. This role exists to remove it, by owning the surface where customers actually meet the product and making it obvious.
The role
You'll own product surface end to end: the interfaces, the APIs behind them, and the data models underneath.
The hard part isn't the CRUD. It's making genuinely complex system behavior legible and fast for the people using it. That takes taste as much as it takes engineering.
The split is roughly [60% frontend and API layer, 40% backend services]. You will not be handed a design file, and you will not be handed a spec. If you want to go deep on distributed systems and never touch a UI, this is the wrong role. If you can't hold your own on the backend either, it's also the wrong role.
We're small and senior. You'll pick the architecture, ship it, watch it in production, and fix what's wrong.
What you'll do
- Ship features across the whole stack. React and TypeScript on top, Python or Go underneath, real data models in between
- Talk to customers directly. Join calls, watch people use what you built, and ship against what you learn
- Sit with platform, data, and ML engineers to figure out what the system can do, then design a product experience around it
- Take things from idea to production, including the testing, deploys, monitoring, and the second version after you learn you got it wrong
- Sharpen the APIs, tooling, and UI patterns so the platform stops needing explanation
What success looks like
- First few weeks: You've shipped something real to production and sat in on a customer call
- First few months: You own a major surface of the product outright, and you're the person who decides how it's built
- Beyond: The platform is noticeably easier to understand than when you arrived, and you're raising the bar on how everyone here ships
What we're looking for
- You've shipped production software end to end and can point at something and say "I built that"
- You're strong on the backend (Python, Go, Java, or similar) and can build a real React/TypeScript app without waiting for a designer
- You have opinions about API design and data modeling, and can defend them
- You've operated things in the cloud (AWS, GCP, or Azure) and debugged them at 11pm
- You make pragmatic calls under ambiguity and know which tradeoffs are worth the argument
- You want to be near customers, not shielded from them
We care about what you've built, not how long you've been building.
Helps, doesn't gate: data-heavy or developer-facing products, distributed systems, ML-adjacent platforms, Kubernetes, observability and performance work, a real interest in AI infrastructure.
Why Join
- Meaningful equity. You're early. The scope and the upside both reflect that.
- The infrastructure layer for agentic AI is being built right now. Very few people get to work on a layer this foundational this early.
- Small, senior team. Your scope is as large as you're willing to make it.
- Real ownership of both architecture and product direction, not a ticket queue.
- Full benefits (medical, dental, vision, 401k). We sponsor H-1B visas and help with immigration.
Apply
Send us (what you've built: a repo, a product, a thing you shipped that you're proud of) at [email protected]. Tell us what was hard about it.
We value builders over résumés. If this role excites you but you don't check every box, apply anyway and show us the work.
We value builders over résumés. If this role excites you but you don't check every box, we still want to hear from you. zaimler is an equal opportunity employer.
Similar Jobs
Financial Services
Design and develop secure, resilient full-stack systems and microservices using Java or Python and AWS. Build digital asset custody components, blockchain integrations, APIs, and cloud applications across the software development lifecycle. Create architecture artifacts, troubleshoot production issues, analyze data, and improve reliability. Lead responsible adoption of AI-assisted engineering tools for coding, testing, reviews, refactoring, and incident analysis while coaching teams on secure, compliant practices.
Top Skills:
Amazon Api GatewayAmazon RdsAmazon S3Amazon SnsAmazon SqsAWSBlockchainCi/CdEthereumEvmJavaMicroservicesPythonTerraform
Fintech • Information Technology • Financial Services
Build, enhance, and support microservices powering financial portfolio construction, data integrations, and real-time operational workflows. Develop platform capabilities, APIs, data pipelines, and infrastructure while collaborating with stakeholders to address business needs. Troubleshoot live production issues, provide critical real-time support, write maintainable code, validate quality, and contribute to a fast-paced interdisciplinary engineering environment.
Top Skills:
AngularAWSAws SqsAzureCassandraDynamoDBGCPGoGraphQLGrpcJavaMicrosoft DynamicsPostgresPythonRabbitMQReactRestSalesforceSQLSQL ServerSybase
Aerospace • Hardware • Robotics • Software • Manufacturing
Develop and deploy full-stack applications supporting purchasing, manufacturing planning, scheduling, test-data analysis, and factory operations. Lead the software development lifecycle, collaborate with non-software stakeholders to define requirements, apply agile and continuous delivery practices, solve complex problems, and mentor engineers. The role involves building scalable platforms across front-end, services, analytics, industrial automation, and AI capabilities.
Top Skills:
Amazon RedshiftApache IcebergAWSDbtDuckdbGoHelmKubernetesMongoDBPostgresPythonReactSparkTerraformTypescript
What you need to know about the San Francisco Tech Scene
San Francisco and the surrounding Bay Area attracts more startup funding than any other region in the world. Home to Stanford University and UC Berkeley, leading VC firms and several of the world’s most valuable companies, the Bay Area is the place to go for anyone looking to make it big in the tech industry. That said, San Francisco has a lot to offer beyond technology thanks to a thriving art and music scene, excellent food and a short drive to several of the country’s most beautiful recreational areas.
Key Facts About San Francisco Tech
- Number of Tech Workers: 365,500; 13.9% of overall workforce (2024 CompTIA survey)
- Major Tech Employers: Google, Apple, Salesforce, Meta
- Key Industries: Artificial intelligence, cloud computing, fintech, consumer technology, software
- Funding Landscape: $50.5 billion in venture capital funding in 2024 (Pitchbook)
- Notable Investors: Sequoia Capital, Andreessen Horowitz, Bessemer Venture Partners, Greylock Partners, Khosla Ventures, Kleiner Perkins
- Research Centers and Universities: Stanford University; University of California, Berkeley; University of San Francisco; Santa Clara University; Ames Research Center; Center for AI Safety; California Institute for Regenerative Medicine
