Sr. Staff Machine Learning Engineer

Flex•New York, NY

10d•$170,000 - $235,000•Hybrid

About The Position

We are seeking an experienced Senior Staff Machine Learning Engineer to join our dynamic team and take a leading role in developing cutting-edge machine learning systems that drive business growth. As a key technical contributor, you will drive the development, deployment, and scalability of machine learning models in a production environment, ensuring they deliver value and performance at scale. You will collaborate closely with data scientists, product teams and engineers to implement state-of-the-art solutions that power our products and services through continuous innovation.

Requirements

Master’s or Ph.D. in Computer Science, Engineering, or a related field.
6+ years of experience as a Machine Learning Engineer, with expertise in building and deploying machine learning models in production environments.
Strong proficiency in Python, or similar programming languages, and experience with ML libraries like TensorFlow, PyTorch, and scikit-learn.
Extensive experience with cloud platforms (e.g., AWS, GCP, Azure) and distributed computing frameworks (e.g., Spark, Kubernetes).
Proven track record of implementing end-to-end machine learning pipelines, from data preprocessing to production deployment and monitoring.
Strong background in model optimization, version control, and CI/CD practices for machine learning.
Excellent problem-solving abilities and the capacity to collaborate with cross-functional teams to deliver high-quality, production-ready systems.

Nice To Haves

Domain expertise in areas like payment risk, fraud detection, or customer success are highly preferred.
Expertise and familiarity with NLP models are considered an asset.

Responsibilities

Own the end-to-end lifecycle of machine learning projects, from data collection and preprocessing to model deployment, monitoring, and maintenance in a production environment.
Build, maintain, and optimize robust data pipelines that support model development, training, and deployment at scale.
Implement machine learning algorithms and models that meet performance, scalability, and reliability requirements in a production setting.
Collaborate with data scientists, engineers, and product teams to design and deploy machine learning systems that address business and product needs.
Continuously monitor and improve model performance, conducting experiments, tuning hyperparameters, and ensuring models meet business objectives.
Leverage distributed computing frameworks and cloud-based platforms to process large-scale datasets efficiently.
Stay up-to-date with the latest advancements in machine learning, software engineering practices, and deployment strategies to keep our systems cutting-edge.