Vinci4d
Member of Technical Staff - Backend Engineer - Data Systems and APIs
3mo ago
160000 –250000 USD / yearUSALeadRemoteapibackenddata processingsimulationpython
Backend engineer responsible for building data generation pipelines and product backend APIs integrating AI models and simulations.
Responsibilities
- We’re looking for a backend engineer who enjoys building systems that sit between data infrastructure and real product features.
- This role spans two major areas:
Other
- We’re building a copilot for hardware. Software engineers have powerful AI tools. Hardware engineers still rely on workflows that take hours or days to simulate and iterate. Vinci is changing that.
- Our platform combines AI, geometry processing, and physics simulation to help engineers validate designs dramatically faster than traditional tools. The system integrates foundation models with simulation engines to produce full-fidelity physical predictions in seconds instead of hours.
- We’re a small team building infrastructure that connects AI models, large-scale simulation data, and production software used directly by engineers.
- Build pipelines that generate and process large datasets used for training and evaluating models
- Manage simulation outputs, geometry data, and experiment artifacts
- Develop tools for validating, transforming, and curating datasets
- Build and maintain APIs used by the Vinci product
- Develop integrations with models, simulation engines, and external tools
- Design services that support the core user workflows of the platform
- You’ll work across the stack with ML engineers, physics researchers, and product engineers.
- We value engineers who are comfortable moving between infrastructure, data systems, and product code, and who enjoy building pragmatic systems that ship.
- Experience building and operating production backend services
- Strong experience designing APIs and service architectures
- Ability to write clean, maintainable, well-tested code
- Experience debugging and improving performance, reliability, and observability
- Comfortable integrating external services, APIs, and internal models
- Ability to work across teams to translate product requirements into system design
- Experience building data pipelines or large-scale processing workflows
- Familiarity with batch processing, distributed systems, or workflow orchestration
- Experience managing large datasets and data transformations
- Comfort working with compute-heavy workloads and long-running jobs
- Experience deploying and operating systems in cloud environments (AWS, GCP, or similar)
- Familiarity with containerized services and modern deployment workflows Ability to design systems that balance throughput, latency, and cost
- We’re a small team and engineers own large pieces of the system. That means:
- Designing systems, not just implementing tickets
- Shipping features that go directly into the product
- Working closely with researchers and customers