Senior Machine Learning Engineer (AI)

TL;DR

Senior Machine Learning Engineer (AI): Building innovative data products and scalable production-ready ML systems with an accent on pipeline design, statistical validation, and model optimization. Focus on implementing real-time monitoring systems, refining training pipelines, and championing ethical AI development.

Location: Boston or New York, USA. Must be able to obtain a gaming license issued by the appropriate state agency as a condition of employment.

Company

A publicly traded technology company (NASDAQ: DKNG) specializing in regulated gaming.

What you will do

• Design and refine scalable pipelines for training, evaluating, and deploying machine learning models.
• Build rigorous testing and statistical validation frameworks to ensure reliability in production.
• Optimize models for computational efficiency, latency, and resource utilization.
• Implement real-time monitoring systems to detect data drift, model decay, and performance degradation.
• Partner with cross-functional teams to design and deploy end-to-end ML applications that scale with business growth.
• Lead technical initiatives to advance ML standards and promote responsible and ethical AI development.

Requirements

• At least 3 years of software engineering experience designing and operating AI/ML systems in production.
• Strong foundation in machine learning and statistics applied to complex, real-world problems.
• Hands-on experience managing the full ML lifecycle: data preparation, feature engineering, deployment, and monitoring.
• Expertise in Python and tools supporting scalable ML systems.
• Experience with modern infrastructure technologies such as Databricks, Kubernetes, and Terraform.
• Ability to obtain a state-issued gaming license as a condition of employment.

Culture & Benefits

• Innovation-driven environment that actively embraces emerging AI technologies.
• Commitment to equal opportunity and diversity regardless of gender, race, nationality, or religion.
• Supportive culture that encourages applying even if not every requirement is met.
• Opportunity to work at the intersection of data, engineering, and experimentation in a high-impact role.