Role Overview

We are seeking an experienced scientific software engineer to contribute to C-Star, our open-source modeling toolkit. C-Star encompasses a wide range of tools and concepts that range from scientific algorithms, data processing pipelines, workflow management, schema and software lifecycle management, and more. The successful candidate will have a strong foundation in Python software development and scientific modeling applications, and the ability to learn quickly and contribute in areas outside their current expertise.

As we are actively incorporating AI into our tools and development workflows, we are seeking a candidate who is eager to utilize the latest AI-assisted tooling to move quickly, while also understanding the fundamentals of good coding practices and displaying a commitment to learning and improving an existing codebase.
The primary impact of this position is the software that you will help design and build. While opportunities to author or co-author scientific publications may present themselves, they are not a core focus of this position.
[C]Worthy is headquartered in Boulder, CO, and a candidate who can work in-office three days per week is preferred. Relocation expenses or fully remote positions may be considered for exceptional candidates.

Key Responsibilities

• Design, build, test, & maintain C-Star software components alongside a multidisciplinary, scientifically-oriented team.
• Quickly generate working software prototypes to address emerging needs and user feedback.
• Independently execute on projects, keeping a focus on high-level goals while maintaining flexibility to diagnose and solve problems as they arise.
• Assist fellow developers via code reviews, design discussions, and testing.
• Support internal and external users via issue review, troubleshooting, tutorials, requirement-gathering, and documentation.
• Contribute to an organizational culture of cooperation, learning, and adaptation to a changing world.

Minimum Qualifications

• Bachelor’s, Master’s, Doctorate degree, or equivalent work experience in a technical field (e.g., oceanography, atmospheric science, physical science, applied mathematics, computer science).
• Understanding of computationally-heavy scientific modeling workflows, their challenges, and typical user needs. Ocean modeling experience is preferred, but similar earth system modeling experience will also be considered.
• Excellent problem-solving and critical-thinking skills; ability to understand both scientific and software systems, and tackle abstract problems in the overlap between them.
• 3+ years of experience writing clean, maintainable, object-oriented software in Python
• Experience with AI-assisted coding tools is strongly preferred, but we will consider candidates with a strong interest in AI-assisted development and a demonstrated ability to learn and apply new technologies quickly.
• Experience working in small, interdisciplinary, fast-moving teams with shared projects and tasks; demonstrated ability to focus on objectives, iterate towards solutions, and adapt to changing requirements.
• Strong written and oral communication skills, including the ability to translate scientific requirements into software specifications and convey technical results to non-specialist audiences.

Preferred Qualifications

• Experience working with multi-dimensional scientific datasets in Python (e.g., netcdf/zarr formats and Pangeo libraries, particularly Xarray, Numpy, Scipy, and Dask).
• Experience with HPC/supercomputer environments (SLURM, MPI, distributed file systems).
• Expertise in TB/PB-scale data engineering.
• Knowledge of workflow management tools (DAGs, state machines, tracking databases).
• Experience building software intended for reproducibility of results (data versioning, schema versioning, logging, audit trails).
• Experience or interest in natural language interfaces for complex scientific problems (e.g. building AI primitives and skills for running ocean models or interrogating results).
• Familiarity with FORTRAN.
• Experience with GPU-accelerated computation (e.g., CUDA, CuPy, JAX, PyTorch) and running or optimizing numerical/modeling codes on GPUs.