Senior Software Engineer, AuditBoard

• If you enjoy building robust software systems and applying ML pragmatically (not experimentally) to solve real customer problems, this role is for you
• 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, TensorFlow, 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

Desirable

• 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
• 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