About the role and why it’s unique:

• As a Staff ML Engineer on the Data Platform team, you’ll be developing and deploying advanced machine learning models and algorithms on a cloud environment
• Implement end-to-end machine learning pipelines, starting from data collection, feature engineering, model training, evaluation, to deployment
• Build frameworks to measure model performance and accuracy in production environments, leveraging techniques such as parameter tuning and model optimization
• Implement and maintain monitoring, alerting, and logging mechanisms to ensure the health and accuracy of Underdog’s ML systems
• Utilize your understanding of machine learning algorithms, including supervised and unsupervised learning, deep learning, reinforcement learning, and ensemble methods, to build production systems
• Work closely with engineering and product teams to ensure seamless integration of machine learning services into Underdog’s data platform
• Collaborate with the data science and quant teams to deploy ML models into production systems
• Mentor junior engineers, lead technical initiatives, and drive results in a fast-paced, dynamic environment
• Lead code reviews, provide constructive feedback, and evangelize best practices to maintain code and data quality
• Research and keep up to date on emerging ML technologies and trends and focus on iteratively implementing them into Underdog’s engineering systems

Who you are:

• At least 7 years of experience building scalable ML model training and inference systems on a cloud environment (e.g. AWS, GCP, Azure)
• Highly focused on delivering results for internal and external stakeholders in a fast-paced, entrepreneurial environment
• Excellent leadership and communication skills with ability to influence and collaborate with stakeholders
• Prior experience with machine learning libraries and frameworks such as TensorFlow, PyTorch, and/or scikit-learn
• Familiarity with containerization and orchestration technologies such as Docker, Kubernetes, or ECS
• Experience with data streaming frameworks such as Apache Kafka, Apache Flink, or Kinesis
• Advanced proficiency with Go, Python, or other OOP languages (at least 2)
• Advanced proficiency with SQL
• Experience with DevOps practices such as CI/CD pipelines, and infrastructure-as-code tools (e.g. Terraform, CDK)

Even better if you have:

• Strong interest in sports
• Prior experience in the sports betting industry
• Experience in building simulation or inference systems

Our targeted compensation rate for this position is between $185,000 and $250,000, depending on experience, plus equity. Think your skills are exceptional and warrant higher pay? Apply anyway! If we agree, we're willing to negotiate.