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Tessera Labs

Software Engineer, Backend

San Jose Office (HQ)$180k–$250kfulltimemidAdded today

About this role

Tessera Labs seeks a mid-level backend engineer to design and maintain scalable APIs, microservices, and data pipelines powering an AI-driven business automation platform. You'll integrate enterprise systems like Salesforce and SAP with AI workflows while owning the architecture and performance of distributed backend systems in a fast-paced startup.

What you'll do

  • Design and develop RESTful APIs and microservices using Python, FastAPI, and SQLAlchemy
  • Build and optimize database schemas, queries, and caching strategies for performance at scale
  • Integrate backend services with AI model servers, vector databases, and enterprise platforms (Salesforce, SAP, Workday)
  • Implement data pipelines and asynchronous processing with message queues and event-driven architectures
  • Identify performance bottlenecks and implement monitoring, logging, and reliability improvements
  • Collaborate with frontend, AI, and product teams while contributing to architectural decisions

What they're looking for

  • Python (FastAPI, SQLAlchemy)
  • PostgreSQL/MySQL and data modeling
  • Redis and message queues (Kafka, RabbitMQ, Pub/Sub)
  • Cloud platforms (AWS, GCP) and cloud storage (S3, Google Cloud Storage)
  • API authentication and security (OAuth2, RBAC, SSO)
  • Distributed systems design and microservices architecture
  • Performance debugging and optimization
  • Testing and code quality best practices
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Tessera Labs

Tessera Labs builds enterprise AI automation platforms that streamline business workflows across systems like Salesforce and SAP using multi-agent AI systems. The company is hiring frontend engineers, backend engineers, AI agent engineers, design engineers, and interns to develop scalable interfaces, APIs, and LLM-driven automation pipelines.

View all jobs at Tessera Labs

Likely interview questions

  • Walk us through your experience designing and scaling REST APIs—what challenges have you faced with throughput or latency?
  • How would you approach integrating an external AI model server into a microservices backend while maintaining fault tolerance?
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