Staff Machine Learning Engineer

About AuditBoard

Having surpassed $200M ARR and continuing to grow, AuditBoard is the leading audit, risk, ESG, and InfoSec 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: AuditBoard is top-rated on G2.com and Gartner Peer Insights.

At AuditBoard, 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 InfoSec 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 seeking a passionate and skilled Machine Learning Engineer to join our effort to push the frontiers of risk management. This role offers the opportunity to work with cutting-edge Large Language Models (LLMs), utilizing techniques like Retrieval-Augmented Generation (RAG), Few-Shot Learning, Prompt Engineering, Fine-Tuning, Semantic Search, and Knowledge Distillation, etc. You will apply your expertise to build advanced AI/ML solutions, enhancing our AuditBoard product portfolio with features like predictive analytics, automated risk assessments, intelligent data extraction, and personalized insights. If you're excited about leveraging modern AI/ML techniques to transform the industry, this role is for you. Join our customer-focused team dedicated to continuous learning and innovation.

Responsibilities

• Build, ship, and own product features end-to-end such as predictive analytics, automated risk assessments, intelligent data extraction, and personalized insights.
• Work with engineers, designers, and product managers to create high-performing product features.
• Design and implement AI solutions using classical ML methods and advanced techniques like LLMs
• Write well-designed, maintainable, and testable code
• Write clear and well-defined design documentation
• Troubleshoot, debug, and resolve software bugs
• Be product-minded and think about the customer
• Stay updated on AI/ML advancements and explore new techniques and tools.
• Participate in an Agile software development life cycle
• Work with Python, JavaScript, Node.JS, Docker, PostgreSQL, Kubernetes, etc

Attributes of a Successful Candidate

• Education: Bachelor’s or Master’s degree in Computer Science, Statistics, Mathematics, or a related field
• Experience: 8 plus years of hands-on experience in developing and deploying machine learning models
• Coding: Ability to write scalable production-quality code
• ML Expertise: Proficiency in classical machine learning methods and familiarity with newer techniques like LLMs
• Programming Languages: Excellent programming skills in Python, Java, or similar languages
• ML Frameworks: Experience with machine learning frameworks such as TensorFlow, PyTorch, Hugging Face, Keras, MXNet, or scikit-learn.
• Search & Ranking: Familiarity with search/information retrieval, and ranking systems
• Communication: Strong communication skills and ability to work collaboratively
• Mindset: Analytically minded with a focus on metrics and evaluation
• Motivation: Motivation to work hard and always be learning

Preferred Qualifications

• Experience with Node.JS and modern ES6 or TypeScript
• Experience working on SaaS web applications
• Basic understanding of distributed systems

Bonus Qualifications

• Docker, Kubernetes experience
• AWS/Azure cloud infrastructure

Job Details

• Salary: CA$184K - CA$276K
• Location Type: Remote
• Employment Type: FullTime

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 strategic goals
• Personal improvement: Stay eager to share insights, se