Lead Backend Engineer

We're looking for a passionate Backend Engineer to build and scale the backend systems that power our AI platform for engineering simulation. You'll work on production-grade APIs, distributed microservices, event-driven architectures, durable long-running workflows, and cloud-native infrastructure. It's a new platform, built from the first commit. This role is ideal for engineers who enjoy solving complex backend problems, building highly scalable systems, and taking ownership of services from design to production. You'll collaborate closely with ML engineers, simulation engineers, and platform engineers to build reliable backend services capable of handling high-throughput AI and simulation workloads in a fast-paced startup environment.

Responsibilities:

• Production-grade REST and gRPC APIs using FastAPI.
• Distributed microservices powering AI and simulation workflows.
• Durable, long-running workflows using Temporal.
• Event-driven systems using Kafka or Pub/Sub.
• Scalable PostgreSQL data models, including vector search.
• Cloud-native, observable services on Kubernetes.

Backend Development:

• Design, develop, and maintain REST APIs (FastAPI) and gRPC services, contract-first.
• Build modular, reusable, type-safe, and well-tested backend services.
• Design PostgreSQL schemas with proper indexing, constraints, and migrations, and optimize queries.
• Build durable, long-running workflows using Temporal: workflows that run for hours, wait for human input, and resume after failures.
• Build asynchronous processing using Kafka, Celery, Redis, or Airflow.
• Integrate backend services with storage such as GCS/S3 and internal platform services.

Distributed Systems:

• Build resilient systems with retries, dead-letter queues, idempotency, and failure recovery.
• Design stateless, horizontally scalable services with both synchronous and asynchronous communication.
• Keep each customer's data isolated at every layer of a multi-tenant platform.

Platform and Cloud:

• Work with GCP services such as GKE, Cloud SQL, Cloud Storage, and Pub/Sub, and with PostgreSQL running in our own containers on Kubernetes.
• Implement caching strategies using Redis.

Reliability and Observability:

• Implement structured logging and instrument services with Prometheus and OpenTelemetry.
• Improve reliability through health checks, readiness probes, graceful shutdowns, and fault tolerance.
• Participate in production debugging, incident response, and performance optimization.

The core requirements for the job include the following:

Backend Engineering:

• 4+ years of professional backend development experience.
• Strong Python skills, including async programming (async/await, concurrency, background workers) with FastAPI.
• Experience with the modern Python service stack: Pydantic, SQLAlchemy (async), Alembic, pytest, and mypy.
• Solid REST and gRPC API design, contract-first (OpenAPI, protobuf, JSON Schema).
• Experience with PostgreSQL, database design, and query optimization; familiarity with vector databases such as pgvector.
• Good understanding of distributed systems and event-driven architectures, with Kafka or Google Pub/Sub.
• Understanding of multi-tenant system design.
• Experience with Redis for caching, task queues, or rate limiting.

Cloud and DevOps:

• Experience with Docker and deploying applications on Kubernetes (GKE preferred).
• Familiarity with CI/CD pipelines (GitHub Actions preferred).
• Understanding of rolling deployments, rollbacks, secrets management, and configuration management.

Good to Have:

• Experience with Temporal or other workflow orchestration tools.
• Experience with engineering simulation or physics: CAE tools, CFD or thermal analysis, solvers such as OpenFOAM or Ansys, or HPC.
• Experience with the transactional outbox pattern and change data capture (Debezium).
• Experience with MCP or LLM agent frameworks such as LangGraph.
• Experience with NoSQL and graph databases.

What We're Looking For:

• Take ownership from design to production.
• Build scalable, reliable, observable, production-ready systems.
• Bring strong debugging, problem-solving, and system design skills.
• Make pragmatic engineering decisions with long-term maintainability in mind.
• Communicate clearly and work independently in a fast-moving startup.
• Eager to learn new technologies and solve challenging backend problems.