About the role

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.

What you’ll do

• 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.
• Candidates with 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.

Key qualifications

• 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.

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