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Core Competencies
Role fitCore Competencies
Use this summary to align your resume positioning with the role.
Demonstrates expertise in building backend architectures and data pipelines for scientific computing, with a strong focus on Python, JAX, and MLOps. Capable of transforming complex research code into maintainable software while ensuring reproducibility and performance optimization.
Highest-signal resume keywords
Python ProgrammingJAX FrameworkBackend Architecture DevelopmentMLOps and ML InfrastructureCI/CD and Testing
ATS Keywords
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Hard Skills
Software EngineeringML EngineeringScientific ComputingData EngineeringAPIs DevelopmentData PipelinesComputational WorkloadsVersioningMonitoringReproducible Workflows
Soft Skills
Curiosity
Tools & Technologies
DockerKubernetesCloud InfrastructureGPU ComputingFastAPI
Industry Keywords
Simulation SoftwareExperimental DataPhysics ModelsML ModelsOptimization
Tech Stack
Tools & technologiesCloudDockerKubernetesPython
About the role
Key responsibilities & impact- Build backend architectures connecting sensor data, physics models, ML models, and simulation workflows
- Turn JAX-based research code into maintainable, production-ready software
- Build pipelines connecting experimental data, simulation, surrogates, and optimization
- Develop infrastructure for model training, evaluation, and reproducibility
- Build APIs, packages, and repositories for long-term production use
- Establish testing, CI/CD, versioning, and monitoring for scientific software
- Identify computational bottlenecks and improve performance
- Contribute to deployment and cloud infrastructure as products mature
- Work directly with scientists and engineers to turn research code into reliable software
- Own substantial technical foundations of the simulation platform
Requirements
What you’ll need- Experience taking technically complex software from prototype to reliable, usable software
- Experience in software engineering, ML engineering, scientific computing, or data engineering
- Python and modern software engineering
- JAX or other numerical and ML frameworks
- Backend architectures and APIs
- Data pipelines and databases
- ML infrastructure and MLOps
- Testing, CI/CD, and reproducible workflows
- Computationally intensive workloads
- Experience with Docker, cloud infrastructure, Kubernetes, GPU computing, FastAPI, or ML tooling is useful
- Experience with JAX, scientific computing, or simulation software is particularly relevant
- Curiosity about the physical world behind the software
Benefits
Comp & perks- Salary and pension
- Qualified employee stock options
- Flexible working arrangements
- Work-life balance / valuing having a life outside work