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# Senior Scientific Software Engineer - Python & Machine Learning

- **Company:** Pauwels Consulting, LLC
- **Location:** United States (Remote available)
- **Experience:** Expert
- **Contract:** Permanent contract
- **Skills:** Application Programming Interfaces (APIs), Automation of Tests, Profiling, Genetic Algorithm, Python (Programming Language), Machine Learning, Performance Tuning, Regression Testing, Release Management, Software Tools, Tensorflow, Software Engineering, Strategies of Testing, Management of Software Versions, High Performance Computing, Pytorch, Backend, Git, Containerization, Slurm, Machine Learning Operations, Api Design
- **Published:** August 24, 2026
- **Apply:** https://arc.dev/remote-jobs/j/redirect/pelg28pe24

## About the Role

* You have 8+ years of software engineering with Python, focusing on API design, library development, and modern packaging like Poetry, uv, or PDM.
 * You possess 5+ years experience training and deploying models with PyTorch or TensorFlow in production environments.
 * You bring 5+ years designing CI/CD pipelines including linting, type checking with mypy, and automated testing.
 * You have 5+ years with Git discipline, semantic versioning, and managing complex merge conflicts in scientific codebases.
 * You bring experience translating biological concepts and experimental workflows into software tools.
 * You're proactive and focused on code correctness and numerical accuracy.
 * You are fluent in English.

Nice to Haves

 * Experience with biological foundation models, fine-tuning, and sequence models for RNA or proteins.
 * Knowledge of multi-framework architecture using lazy imports and abstract backend patterns.
 * Hands-on experience with HPC environments, SLURM, Singularity, and Apptainer.
 * Understanding of genetic algorithms and core bioinformatics data formats.

## Description

* Design and evolve public Python APIs while maintaining stable internal structures and backward compatibility.
 * Translate biological research code and experimental workflows into reusable and scalable software capabilities.
 * Deploy machine learning models using frameworks like PyTorch and TensorFlow while managing complex dependency conflicts.
 * Implement multi-level testing strategies including unit, integration, and numerical regression tests against scientific fixtures.
 * Manage production CI/CD pipelines incorporating linting, type checking, and platform compatibility checks.
 * Oversee release management using semantic versioning, deprecation strategies, and comprehensive documentation.
 * Optimize computationally intensive workflows through profiling, benchmarking, and containerization in HPC environments.


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