Senior Machine Learning Engineer

DraftKings Inc.

3d•$134,400 - $168,000

About The Position

As a Senior Machine Learning Engineer on the Lottery Intelligence Team, you will build and scale machine learning solutions that turn complex operational data into accurate, reliable customer experiences. You will lead development of computer vision and multimodal models, taking solutions from experimentation through real-world deployment. In collaboration with Engineering and cross-functional teams, you will improve model performance, strengthen customer trust, and help expand lottery fulfillment capabilities into new markets nationwide.

Requirements

Bachelor’s degree in Computer Science, Engineering, Mathematics, Data Science, or a related field, or equivalent practical experience.
At least 3 years of machine learning engineering experience.
Proven experience developing, deploying, and maintaining machine learning models in production environments.
Strong proficiency in Python and software engineering best practices, including automated testing, data validation, and asynchronous programming.
Hands-on experience with computer vision, multimodal or vision-language models, and machine learning frameworks such as PyTorch, Hugging Face Transformers, PEFT, or vLLM.
Experience with model fine-tuning, prompt design, performance evaluation, and image processing tools such as OpenCV.
Experience with cloud-based machine learning platforms and familiarity with containerization, orchestration, and monitoring tools such as Docker, Kubernetes, GitHub Actions, or Datadog.
Strong technical ownership, problem-solving, and communication skills, including the ability to lead complex initiatives, explain technical decisions, and collaborate across teams.

Nice To Haves

Google Cloud experience is preferred.
Familiarity with Kotlin, Java, message-driven architectures, or Protocol Buffers.
Experience in lottery, gaming, financial technology, or other regulated industries.

Responsibilities

Design, train, and optimize computer vision and multimodal machine learning models that transform complex inputs into accurate, structured data.
Fine-tune and evaluate multimodal models using techniques such as LoRA and hyperparameter optimization to improve accuracy, speed, and cost efficiency.
Build and maintain scalable data pipelines for preparing, validating, and organizing production data for model training, testing, and evaluation.
Develop evaluation frameworks and performance metrics to compare models, measure accuracy, and guide deployment decisions.
Deploy and manage machine learning models on scalable cloud infrastructure, using automated scaling, monitoring, and controlled rollouts to maintain reliable performance.
Integrate machine learning models into real-time processing systems and enhance computer vision capabilities, including image preprocessing, barcode decoding, and optical character recognition.
Partner with Engineering, Fulfillment Operations, and other cross-functional teams to identify opportunities, improve operational workflows, and strengthen lottery fulfillment systems.
Elevate engineering standards through code reviews, technical documentation, automated testing, and mentorship, while contributing to broader team data infrastructure.