Flutter Entertainment-posted 2 months ago
$116,000 - $152,250/Yr
Mid Level
Hybrid • New York City, NY
5,001-10,000 employees
Amusement, Gambling, and Recreation Industries

We're looking for a Machine Learning Engineer to join our growing team and help design, build, and deploy machine learning systems that power real-world applications. In this role, you'll work closely with data scientists, engineers, and product managers to bring models from experimentation to production and ensure they perform reliably at scale. You'll contribute across the ML lifecycle-including feature engineering, model training, evaluation, deployment, and monitoring-while growing your skills in software development, ML Ops, and scalable infrastructure. If you're excited by this challenge and want to work within a dynamic company, then we'd love to hear from you.

  • Collaborate with data scientists to implement and optimize machine learning models for production use.
  • Develop and maintain pipelines for data preparation, training, and model deployment.
  • Build tools and services to support real-time and batch inference workloads.
  • Translate product and business requirements into ML-driven solutions.
  • Participate in agile workflows, including sprint planning, code reviews, and design discussions.
  • Work with engineers and analysts to ensure data integrity and efficient feature computation.
  • Implement monitoring and alerting to track model performance and detect issues such as data drift.
  • Write maintainable, testable code and follow best practices in version control and documentation.
  • Help automate training, deployment, and retraining workflows using ML Ops tools.
  • 2-4 years of experience in software engineering, machine learning, or data science.
  • Proficiency in Python, with exposure to ML libraries (Scikit-learn, TensorFlow, or PyTorch).
  • Solid understanding of data structures, algorithms, and software engineering principles.
  • Hands-on with SQL and comfortable working with large datasets.
  • Familiarity with distributed computing (Apache Spark preferred).
  • Exposure to ML deployment & monitoring practices or strong interest in learning them.
  • Experience with cloud services (AWS preferred, GCP or Azure also valuable).
  • Experience with containerization (Docker, Kubernetes is a plus).
  • Familiarity with orchestration/ML Ops tooling (SageMaker, MLflow).
  • Understanding of model evaluation metrics and techniques for improving generalization.
  • Interest in or experience with real-time ML systems, recommendation engines, or NLP.
  • Array of health plans including programs for fertility and family planning, mental health support, and fitness benefits.
  • Generous paid time off (PTO & sick leave).
  • Annual bonus and long-term incentive opportunities (based on performance).
  • 401k with up to a 5% match.
  • Commuter benefits.
  • Pet insurance.
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