Principal Machine Learning Engineer

How you'll make an impact?

• Design and build scalable platforms for training and inference of ML models
• Develop and integrate data pre-processing and post-processing workflows for seamless model deployment
• Build robust model monitoring services on top of the inference platform to ensure optimal performance
• Create platforms for large-scale model evaluation and grading
• Develop advanced tools for model experimentation to accelerate innovation
• Design and implement sampling strategies to effectively assess model performance
• Oversee the entire ML lifecycle, including design, experimentation, development, deployment, monitoring, and maintenance
• Develop reusable workflows for Data Science models and integrate them with production systems, ensuring efficiency and minimal redundancy
• Deploy production-ready code and actively participate in code reviews to maintain high-quality standards
• Refactor services to improve code quality, runtime efficiency, and resource optimization
• Build automation and active learning frameworks to streamline model retraining processes
• Lead and mentor a team of software engineers, fostering a collaborative environment and providing guidance to help them reach their full potential

What we're looking for?

• Minimum of 8+ years of professional experience in machine learning and related domains
• Deep understanding of the machine learning ecosystem and strong experience in monitoring models and data in production environments
• Proficiency in implementing sampling strategies for diverse models and use cases
• Skilled in grading models at scale to assess and optimize performance
• Proven experience in designing and developing distributed training and inference platforms using distributed computing frameworks like PySpark, Kubeflow, and Kubernetes.
• Extensive experience in Python programming, and strong familiarity with Docker for containerized application development
• Hands-on experience with tools such as MLFlow, TensorBoard, and Weights & Biases (WandB) for evaluating model performance
• In-depth knowledge of MLOps practices and cloud platforms like AWS, GCP, and Azure
• Expertise in handling large datasets for training, including experience with HDFS, Data Lakes, and both SQL and NoSQL databases
• Bachelor's / Master’s Degree in Computer Science, Mathematics & Computing, Electrical Engineering, or a related field

What you'll love:

• Comprehensive Medical, Dental, and Vision Coverage: 100% coverage for employees and 80% for their spouses and children 
• Health Reimbursement Account (HRA): 100% funded by AiDash to cover medical deductibles 
• 401(k) Plan: Begin contributing after three months of employment to prepare for your future. Currently, no company match is offered 
• Parental Leave: Supportive parental leave with 16 weeks for primary caregivers and 4 weeks for secondary caregivers 
• Generous Vacation Policy: Accrue 20 vacation days per year, plus enjoy your Birthday off!