Principal Machine Learning Engineer

About AiDash

AiDash is making critical infrastructure industries climate-resilient and sustainable with satellites and AI. Using our full-stack SaaS solutions, customers in electric, gas, and water utilities, transportation, and construction are transforming asset inspection and maintenance -- and complying with biodiversity net gain mandates and carbon capture goals. Our customers deliver ROI in their first year of deployment with reduced costs, improved reliability, and achieved sustainability goals. Learn more at www.aidash.com.

We are a Series C climate techstartup backed by leading investors (including National Grid Partners, G2 Venture Partners, Lightrock, BGV, Marubeni, among others), and by our customers-turned-advocates (Duke Energy & National Grid Partners, among others)! We have been recognized by Forbes two years in a row as one of "America's Best Startup Employers". We are also proud to be one of the few climate software companies in Time Magazine's "America's Top GreenTech Companies2024".

Join us in creating a greener, cleaner, and safer planet from space!

The Role

We are looking for a Principal Machine Learning Engineer to develop and enhance our ML infrastructure and platforms, streamlining the process of building, deploying, and monitoring machine learning models. In this role, you will work with cutting-edge technologies, rapidly expanding your knowledge and skills. You will collaborate with diverse teams across the company, and will see the direct impact of your contributions on our products and customers.

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!