Facebook pixel

Machine Learning Engineer
Confirmed live in the last 24 hours
Remote • United States
Experience Level
Desired Skills
Computer Vision
Data Analysis
Data Science
Product Design
Natural Language Processing (NLP)
  • BS, MS, or PhD in Computer Science, Mathematics, Statistics, or other quantitative fields or related academic/work experience
  • 3+ years of experience building and deploying ML/DL models or systems
  • Industry experience of applying scientific methods to solve real-world problems on consumer scale data
  • Strong analytical and problem-solving skills. You thrive in ambiguous environments, get excited about finding solutions to complex problems, and then execute on them
  • Ability to work autonomously and lead initiatives across multiple product areas and communicate findings with leadership and product teams
  • Proven ML and software engineering skills across languages including Python, R, Swift etc
  • Experience with ML software tools and libraries (e.g., Scikit-learn, Keras, PyTorch, Huggingface, Haystack, Fastai, TensorFlow, Nvidia, Nbdev.)
  • Experience with MLOps pipelines
  • MS, PhD in Computer Science or related field with research in machine learning
  • Familiarity with large-scale data processing and distributed systems
  • Experience with one or more of the following: statistics, causal inference, natural language processing, computer vision, bayesian reasoning, recommendation systems, search and ranking, learning from semistructured data, graph learning, reinforcement or active learning, ML software systems, machine learning on mobile devices
  • Design, explore, build, evaluate, deploy and iterate on large scale ML systems
  • Perform model explainability analysis
  • Evaluate the performance of ML systems against business objectives
  • Work with large scale data systems
  • Set up and refine the ML tech stack, and build systems that help Underdog personalize their users' experience
  • Work with Product, Design, Infrastructure, and Frontend teams to bring your models, and features to life
  • Contribute across the data science and ML development stack: ideation, opportunity sizing, prototyping, testing, and deployment
  • Data Scientists will analyze data used by both our Sportsbook and Fantasy applications
Underdog Fantasy

201-500 employees