Machine Learning Engineer - Applied Scientist, AI Figure Generation

October 20

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Science Illustration • Science Communication • Technology • Startup • SAAS Startup

201 - 500

💰 Pre Seed Round on 2018-03

Description

• Design and execute multi-quarter AI/ML initiatives that deliver measurable technical, organizational, or business impacts in our Figure generation domain. • Oversee the performance and continued optimization of the figure generation model system: build machine learning models to improve design understanding, and extract user intent and context to deliver accurate, relevant, and personalized figures for users. • Prototype, optimize, and productionize ML models that help deliver key results. • Evaluate performance of figure generation systems and models end to end. • Influence the company’s ML system and data infrastructure to power figure creation to make it faster for our users to create communication materials. • Collaborate with product managers, scientists, full-stack and platform engineers, and designers on product teams. • Communicate with business, data, and engineering counterparts to clarify requirements, provide feedback, and share discovered data stories with stats, charts, and formal presentations. Propose recommendations to maximize business impact.

Requirements

• Extensive industry experience as an ML engineer with expert level knowledge in one or more areas: Object Detection and Image Segmentation, Computer Vision, Deep Learning, Transfer Learning, VLM, Multi-Modal or Generative model or similar. • Hands-on experience with deep learning frameworks such as PyTorch and TensorFlow. • Experience with distributed model training • Experience developing custom model architectures. • Excellent programming skills with one or more of the following languages: python, scala, java. • Expertise with operationalizing, monitoring, and scaling machine learning models and pipelines in cloud ecosystems. • Previous experience working cross-functionally with product and engineers to deliver solutions with complex requirements in an agile environment.