Lead Software Engineer - Machine Learning

August 31

Apply Now

Receive Emails with Similar Jobs

DroneDeploy

WebsiteLinkedInAll Job Openings

Drones • Software • Mapping • Reality Capture • Digital Twins

201 - 500

💰 Series F on 2021-01

Description

• Lead the design, development, implementation and improvement of innovative ML models. • Facilitate the development of robust and scalable ML infrastructure and operations. • Work closely with Computer Vision and DevOps teams for seamless deployment and management of ML models. • Train, improve, evaluate, integrate, and deploy ML models for computer vision use cases. • Optimize the performance of ML models and systems for speed, accuracy, and resource efficiency. • Collaborate with cross-functional teams to understand business requirements and deliver ML solutions.

Requirements

• Bachelor’s or Master’s degree in Computer Science, Engineering, or a related field with 5+ years of professional experience in ML Data Science and MLOps. • Experience designing, building, deploying, debugging and maintaining large-scale production ML systems to solve computer vision problems. • Ability to timebox experiments, iterate effectively and leverage excellent problem-solving skills to triage routes to success. • Experience with modern ML in Pytorch, Keras, TensorFlow or equivalent. • Experience building and improving models like Mask2former, MaskRCNN, Resnet, Unet. • Experience running and monitoring multiple ML experiments in cloud environments concurrently. • Experience using and extending MLOps platforms (e.g. WandB, Vertex AI, Metaflow, mlflow, Databricks) • High degree of comfort with UNIX and training and inference on cloud instances cloud platforms (e.g., AWS, Google Cloud, Azure) and containerization technologies (e.g., Docker, Kubernetes). • Experience with CI/CD tools (e.g., Jenkins, GitHub CI). • Experience as a productive remote employee able to overlap standup AM meetings PST. • Strong communication and leadership skills.

Benefits

• Comprehensive health benefits • Flexible time away from work • Support for ongoing professional development