Staff Software Engineer, Machine Learning Infrastructure

New York City, NY or Remote

Applications have closed

Dandy

Go Digital. We make digital dentistry easy; go from impression molds to digital scans at no cost with Dandy. Get started today!

View company page

About Dandy

Dandy is transforming the massive ($200B) but antiquated dental industry. Backed by some of the world's leading venture capital investors, we are on an ambitious mission to integrate and simplify every function of the dental practice through technology. By building the operating system for every dental office in America, Dandy is empowering dentists with technology, innovation, and world-class support to achieve more for their practice, their people, and their patients.

About the Role

Dandy is hiring a team of top software engineers to build digital products and scale our managed marketplace.  As one of Dandy's first Machine Learning Infrastructure engineers, you will play a key role in building our tech team and foundations. You'll constantly be challenged to learn new technologies, establish best practices, and be given the freedom to solve problems on your own and learn by doing.

We are creating next-generation experiences across the newly 3D-digitized dental stack, so our machine learning platform is critical to our success. As a Machine Learning Infrastructure Engineer, you will be key to establishing the infrastructure our product teams will use - ultimately enabling & expanding our machine learning capabilities.

Relevant Technologies: MLflow, Kubeflow, Airflow, DataRobot

What You’ll Do

  • Build backend infrastructure to perform scalable training, evaluation, and inference in the cloud and client-side infrastructure to perform efficient inference in the cloud and on the edge devices
  • Build comprehensive data management systems for scalable data collection, labeling, processing, and evaluation
  • Work with product teams and engineers to make applications of machine learning ubiquitous to Dandy
  • Be a representative for how to apply machine learning and related techniques throughout the engineering and product organization
  • Work with product and research teams to identify opportunities for improvement in our current product line and for enabling upcoming product lines

What We’re Looking For

  • 8+ years of experience, 3+ in ML Infrastructure
  • Proven experience building scalable Machine Learning Infrastructure that made it into a meaningful production system
  • Experience in building Distributed Systems with Cloud Infrastructure (AWS/Azure/Google Cloud)
  • Experience with MLOps/DevOps best practices for deployment and monitoring of ML models in production environments

Bonus Points For

  • Experience with hyper growth start-up
  • Experience with 3D and Computer Vision 
  • Experience working with remote teams

What Benefits We Offer

  • Fully sponsored best in class healthcare including medical, dental, and vision
  • Competitive salary and equity packages
  • 401k program

Dandy is proud to be an equal opportunity employer. We are committed to building a diverse and inclusive culture and celebrate authenticity. We do not discriminate on the basis of race, religion, color, national origin, gender, gender identity, sexual orientation, age, marital status, disability, protected veteran status, or any other legally protected characteristics.

Tags: Airflow AWS Azure Computer Vision Data management DataRobot DevOps Distributed Systems Engineering GCP Google Cloud Kubeflow Machine Learning MLFlow ML infrastructure ML models MLOps Research

Perks/benefits: Career development Competitive pay Equity Health care Salary bonus

Regions: Remote/Anywhere North America
Country: United States
Job stats:  17  0  0

More jobs like this

Explore more AI, ML, Data Science career opportunities

Find even more open roles in Artificial Intelligence (AI), Machine Learning (ML), Natural Language Processing (NLP), Computer Vision (CV), Data Engineering, Data Analytics, Big Data, and Data Science in general - ordered by popularity of job title or skills, toolset and products used - below.