Junior Software Engineer – Scientific Computing (C++)

Axle is seeking a Junior Software Engineer with a solid foundation in C++ and a genuine enthusiasm for LLM-based development workflows. This role sits at the intersection of traditional high-performance software engineering and the rapidly evolving landscape of AI-assisted coding tools and large language model (LLM) integration.

You will work as part of a collaborative, cross-functional team building scalable software systems for compute-intensive research environments. A key part of your day-to-day will involve leveraging LLM-based tools — such as AI coding assistants, prompt-driven code generation, and LLM-integrated pipelines — to accelerate development and explore novel solutions to engineering challenges.

While C++ experience is important for understanding the performance-critical systems you will work on, we place equal emphasis on your ability to work fluidly with LLM tools and your curiosity about how AI-assisted development can reshape software engineering practice. You will learn domain-specific aspects of the work on the job with support from senior engineers.

Key Responsibilities

• Develop and maintain C++ software components for scientific and computational workloads, with a focus on correctness and maintainability.
• Integrate and leverage LLM-based tools (e.g., GitHub Copilot, Claude, GPT-4, or similar) throughout the development lifecycle — from code generation and review to documentation and debugging.
• Build and experiment with LLM-assisted pipelines for automating repetitive engineering tasks, code scaffolding, and developer productivity tooling.
• Collaborate with senior engineers and researchers to prototype and evaluate LLM-integrated solutions to complex software problems.
• Contribute to testing, CI/CD workflows, and code documentation — using AI tools to maintain quality and delivery speed.
• Stay current with the fast-moving LLM tooling ecosystem and proactively share learnings with the team.

Note: Some projects involve image-based data and scientific computing workflows. Prior expertise in these areas is not required; you will develop this knowledge with support from the team.

Required Skills & Qualifications

• 1–3 years of professional experience (or equivalent academic/research experience) in software development.
• Working knowledge of C++, including comfort reading, debugging, and contributing to C++ codebases.
• Demonstrated experience using LLM-based development tools (e.g., Copilot, Cursor, Claude, ChatGPT, or similar) as part of a regular development workflow.
• Familiarity with prompt engineering concepts and an understanding of how to effectively direct LLMs for code generation, review, or explanation tasks.
• Working knowledge of Linux development environments.
• Basic experience with Python for scripting, tooling, or working with LLM APIs (e.g., OpenAI, Anthropic).
• Strong problem-solving skills and eagerness to learn within a fast-evolving technical landscape.

Preferred Qualifications

• Experience building or integrating LLM-powered features into applications or developer tooling (e.g., RAG pipelines, tool-calling agents, code-aware assistants).
• Familiarity with LLM APIs (OpenAI, Anthropic, Hugging Face) or open-source models (LLaMA, Mistral, etc.).
• Understanding of modern C++ (17/20) idioms and experience with performance-oriented development.
• Exposure to scientific computing, numerical methods, or high-performance computing concepts.
• Experience with parallel computing concepts (multithreading, vectorization, OpenMP).
• Familiarity with modern build systems (CMake, Bazel) and container technologies (Docker).
• Interest in evaluating and benchmarking LLM output quality for engineering tasks.
  

What We Emphasize

• Curiosity about LLM-assisted development and a desire to push the frontier of AI-augmented engineering.
• Solid C++ fundamentals — enough to contribute meaningfully to performance-sensitive systems.
• Learning and growth over prior domain specialization — we care more about how you learn than what you already know.
• Mentorship and close collaboration with experienced engineers.
• Clear ownership of well-scoped technical components from day one.