Principal AI & Machine Learning Engineer

As a Principal AI & Machine Learning Engineer at fabric, you will own AI/ML initiatives from end-to-end. This includes everything from framing complex problems, designing data-driven solutions, to training and deploying models into production. You will also lead MLOps efforts, develop robust ML systems, and collaborate cross-functionally with engineering and product teams.

Candidates for the Principal AI & Machine Learning Engineer position at fabric should have over eight years of experience in AI/ML roles, particularly in high-growth startup environments. A strong track record of building and scaling machine-learning solutions, expertise in programming languages such as Python and Spark, and a relevant advanced degree (PhD or MS) are essential for this role.

To excel as a Principal AI & Machine Learning Engineer at fabric, you should possess strong programming skills in languages and tools such as Spark, Python, TensorFlow, PyTorch, MLFlow, Airflow, Docker, and Kubernetes. These skills are crucial for developing and deploying scalable ML systems effectively.

Fabric fosters a fast-paced, fun, and collaborative environment, encouraging innovation and creativity. As a Principal AI & Machine Learning Engineer, you’ll work closely with a team of talented individuals who are all dedicated to the company's mission and invested in each other’s personal and professional growth.

The base salary for a Principal AI & Machine Learning Engineer at fabric ranges from CAD $170K to $225K, depending on various factors such as qualifications and experience. Additionally, the role may offer performance bonuses, equity options, and comprehensive benefits including medical, dental, and wellness programs.

MLOps is crucial for deploying machine learning models into production smoothly and efficiently. I have extensive experience establishing MLOps pipelines, which involves testing, monitoring, and continuous improvement of ML models. Highlighting specific projects where you implemented MLOps can demonstrate your hands-on skills.

In feature engineering, I emphasize understanding the business context of the data. I utilize both automated and manual techniques to create meaningful features and improve model performance. Discuss specific instances where you effectively used feature engineering to enhance model accuracy.

I encountered a similar situation where a model did not meet performance benchmarks. Through thorough analysis, I realized the importance of data quality and feature relevance. I learned to adapt models to incorporate feedback and continuously iterate to enhance outcomes, which is essential in your role.

I actively engage with online courses, attend industry conferences, and read current research papers. Additionally, I follow thought leaders on platforms like LinkedIn and Twitter. Sharing specific examples of recent trends you’ve integrated into your work can highlight your proactive nature.

Collaboration can be challenging due to differing priorities, but I’ve found that open communication and establishing a common goal help in overcoming these hurdles. Detailing an instance where you successfully bridged gaps between teams can show your adaptability.

Model testing is crucial; I typically employ a combination of k-fold validation, A/B testing, and backtesting strategies to ensure model robustness. Discussing your systematic approach to validation can reassure interviewers of your thoroughness and attention to detail.

The future of AI/ML in commerce involves personalization, predictive analytics, and automated decision-making processes. Sharing your vision along with current innovations can demonstrate your forward-thinking mindset and alignment with fabric’s goals.

Certainly! I developed a recommendation engine that included data collection, feature engineering, model training, and deployment. Outlining the technical steps and the impact of the project can illustrate your comprehensive experience in handling end-to-end solutions.

I have experience with TensorFlow and PyTorch; I prefer PyTorch for its flexibility and ease of use in prototyping and research environments, while TensorFlow excels in production settings. Explaining your choice based on project requirements can demonstrate thoughtfulness in using technology.

I embrace feedback as a vital component of project evolution. Engaging stakeholders early in the process allows for integrated suggestions that can enhance the overall outcome. Sharing an example of how you acted on feedback can illustrate your collaborative nature.