Senior Software Engineer II- Machine Learning

Who We Are

Having surpassed $300M ARR and continuing to grow, Audit Board is the leading audit, risk, ESG, and Info Sec platform on the market. More than 50% of the Fortune 500, including 7 of the Fortune 10, leverage our award‑winning technology to move their businesses forward with greater clarity and agility. And our customers love us:
Audit Board is top-rated on  and Gartner Peer Insights.
At Audit Board, we inspire each other to innovate and are proud of what we are producing. We spend each day thinking of new ways to help our customers and contribute to the greater good of our company and our surrounding communities. We are all about assisting each other and breaking through barriers to create the most loved audit, risk, ESG, and Info Sec platform by our customers.

This is how we have become one of the 500 fastest-growing tech companies in North America for the sixth year in a row, as ranked by Deloitte!

Why This Role is Exciting

We are looking for a Senior Software Engineer with strong machine learning experience to help build and scale intelligent, production‑grade systems that power our risk and compliance platform. In this role, you’ll work at the intersection of software engineering and applied machine learning, shipping real customer‑facing features that leverage both classical ML techniques and modern approaches like Large Language Models (LLMs).

You’ll be embedded in a product engineering team, owning systems end-to-end—from API design and data pipelines to model integration, evaluation, and long‑term maintainability. If you enjoy building robust software systems and applying ML pragmatically (not experimentally) to solve real customer problems, this role is for you.

• Design and implement AI‑powered systems using a mix of classical ML techniques and modern LLM‑based approaches, frequently leveraging managed Azure AI/ML services as building blocks.
• Apply a range of techniques—from classical ML to LLM‑based approaches (RAG, prompt engineering, fine‑tuning, semantic search)—with a strong focus on reliability, performance, and maintainability.
• Collaborate closely with product managers and designers to deliver high‑quality, customer‑focused features.
• Write clean, testable, well‑documented code and contribute to shared engineering standards.
• Author clear design docs that explain system behavior, tradeoffs, and long‑term implications.
• Debug and resolve production issues across application code, data, and ML components.
• Evaluate ML systems using metrics and real‑world signals, and iteratively improve them.
• Participate fully in an Agile development lifecycle, contributing to planning, reviews, and retrospectives.
• Stay current on ML and software engineering best practices, adopting new tools thoughtfully and pragmatically.

• Bachelor’s or Master’s degree in Computer Science, Engineering, Mathematics, or a related field—or equivalent practical experience.
• 4+ years of professional software engineering experience, with meaningful exposure to machine learning in production systems.
• Strong ability to design and build scalable, production‑quality software.
• Excellent programming skills in Python.
• Hands‑on experience applying machine learning models in real systems, including model integration, inference, and evaluation.
• Familiarity with ML frameworks such as PyTorch, Tensor Flow, Hugging Face, or scikit‑learn.
• Experience or interest in search, information retrieval, ranking, or recommendation systems.
• Product mindset: you care about user impact, not just technical elegance.
• Strong communication skills and comfort working cross‑functionally.

• Experience with Node.
  
  JS and Type Script.
• Experience working on SaaS web applications.
• Basic understanding of distributed systems.
• Bonus:
  Docker, Kubernetes experience, AWS/Azure cloud infrastructure.

Our Company Values

• Customer obsession:
  Apply relentless focus on listening to and understanding customers as the core of everything we do.
• Win, together:
  Drive to be the best while supporting each other’s success.
• Gritty resilience:
  Thrive in a fast‑paced and dynamic environment, balancing immediate priorities with big‑picture…