Parallel Domain builds synthetic data and simulation platforms for autonomous vehicle and robotics teams. The Product Engineering team owns the customer-facing product and service layer: the self-serve platform for productizing ML reconstruction pipelines, our high-fidelity simulation-as-a-service product, the web applications and dashboards customers interact with directly, and the centralized backend services and APIs that connect them.
We're hiring a Senior Software Engineer to own delivery across this stack. You'll design and build features and services end to end - from API design through to production - and take real ownership of the reliability and quality of what you ship. Our stack spans Python backend services, RESTful and gRPC APIs, relational databases, React frontends, and data pipelines. Engineers who thrive here are comfortable across multiple layers of the stack, move well from a requirement to a design, and take operational ownership of what they build.
Responsibilities
Expand and maintain customer-facing services and APIs. You'll build and improve the services customers interact with directly: interactive services and async processing pipelines, web app integrations, and APIs that connect them.
Design and implement APIs and data models. You'll architect and implement RESTful API endpoints that cleanly separate data from business logic, are well-documented, and are built to the standards customers and field engineers depend on.
Collaborate across product, SRE, and field engineering. You collaborate with product managers and technical stakeholders to understand requirements and clarify interface design. You partner with SRE on deployment and operational concerns.
Own features end to end. You'll design, implement and ship solutions, and keep them running. You're not done when the PR merges.
Debug production issues and improve service reliability. You'll contribute to observability, alerting, and the ongoing health of the services your team operates. You participate in incident response and drive root-cause improvements.
Uphold and contribute to engineering standards. You follow and help shape team standards around testing, code quality, API documentation, dependency management, and service structure. You leave the codebase better than you found it.
What You Need to Succeed
Track record of delivering complex backend features. You've owned non-trivial backend projects end to end - from design through to production - across APIs, databases, and service integrations.
API design experience. You're comfortable designing RESTful APIs: resource modeling, separation of data and business logic, authentication, versioning, and documentation. You understand what makes an external-facing API maintainable.
Python web frameworks and relational databases. Hands-on experience with Flask, FastAPI, or equivalent frameworks; PostgreSQL or similar; ORM patterns; and writing production-grade backend code.
Containerized deployment. Experience deploying, operating, and debugging services on Linux using Docker and Kubernetes.
Production service ownership. You've owned services in production: monitoring, alerting, on-call response, and the follow-through after an incident.
Requirement gathering and collaboration. You can work with product and customer-facing stakeholders to clarify requirements, surface interface design questions early, and produce concrete designs before implementation starts.
Skills
Languages and Frameworks. Python, FastAPI, Flask, PostgreSQL, React, TypeScript
Systems & Infrastructure. AWS (S3, RDS, Lambda, EKS), Docker, Kubernetes, GitHub Actions, Jenkins, gRPC, Proto
Domain Experience. RESTful API design and documentation. Production service ownership. Data pipeline operation. Observability tooling (Prometheus, Grafana). Experience in simulation, ML pipelines, or autonomous systems is an advantage.
Bonus Points
Experience with event-driven systems and message brokers (e.g., Redis, SQS, RabbitMQ)
Experience with workflow orchestration tools (e.g., Apache Airflow)
Familiarity with load balancers, HTTP proxies, or service mesh patterns
Experience with ORMs such as SQLAlchemy
Background supporting production monitoring databases and alerting infrastructure
Primary ownership of a production React web application, including architecture, state management, and deployment
Experience with frontend testing, build tooling, or performance optimization
What Makes a Great Candidate
Skills Required
- Track record of delivering complex backend features end-to-end (APIs, databases, service integrations)
- API design experience (RESTful APIs, resource modeling, authentication, versioning, documentation)
- Hands-on experience with Python web frameworks (Flask or FastAPI) and relational databases (PostgreSQL) and ORM patterns
- Containerized deployment and operations on Linux using Docker and Kubernetes
- Production service ownership: monitoring, alerting, on-call response, incident follow-through
- Collaboration and requirement gathering with product, SRE, and customer-facing stakeholders
- Experience with frontend technologies (React, TypeScript) and web application integration
- Experience with cloud infrastructure and services (AWS: S3, RDS, Lambda, EKS)
- Experience with gRPC and Protocol Buffers
- Experience with observability tooling and data pipeline operation (Prometheus, Grafana)
- Experience in simulation, ML pipelines, or autonomous systems
- Experience with event-driven systems or message brokers (Redis, SQS, RabbitMQ)
- Experience with workflow orchestration tools (Apache Airflow)
- Familiarity with ORMs such as SQLAlchemy
- Primary ownership of a production React web application (architecture, state management, deployment)
What We Do
Training and testing autonomous systems in the real world is a slow, expensive and cumbersome process. Parallel Domain is the smartest way to prepare both your machines and human operators for the real world, while minimizing the time and miles spent there. Connect to the Parallel Domain API and tap into the power of synthetic data to accelerate your autonomous system development. Parallel Domain works with perception, machine learning, data operations, and simulation teams at autonomous systems companies, from autonomous vehicles to delivery drones. Our platform generates synthetic labeled data sets, simulation worlds, and controllable sensor feeds so they can develop, train, and test their algorithms safely before putting these systems into the real word. #syntheticdata #autonomy #AI #computervision #AV #ADAS #machinelearning
