Machine Learning Engineer responsible for developing and deploying advanced ML and AI solutions at Zendesk. Collaborating with stakeholders to deliver impactful business outcomes using latest machine learning technologies.
About the role
- Lead Machine Learning Engineer developing AI-powered solutions to transform risk management at Capital One. Collaborating with cross-functional teams to deliver innovative products using advanced machine learning techniques.
Responsibilities
- Partner with a cross-functional team of engineers, data scientists, product managers, and designers to deliver AI-powered products that change how our associates work and provide value to our customers.
- Design, develop, test, deploy, and support AI software components utilizing machine learning models, including model evaluation and experimentation, large language model inference, similarity search, guardrails, governance, observability and agentic AI.
- Fine-tune, develop and evaluate machine learning and foundation models.
- Collaborate as part of a cross-functional Agile team to create and enhance software that utilizes state-of-the-art AI and ML capabilities.
- Contribute thought leadership and technical vision to the long term roadmap of pioneering AI systems at Capital One.
- Leverage a broad stack of Open Source and SaaS AI technologies.
- Inform your ML infrastructure decisions using your understanding of ML modeling techniques and issues.
- Retrain, maintain, and monitor models in production.
- Construct optimized data pipelines to feed ML models.
- Ensure all code is well-managed to reduce vulnerabilities, models are well-governed from a risk perspective, and the ML follows best practices in Responsible and Explainable AI.
Requirements
- Bachelor's degree
- At least 6 years of experience designing and building data-intensive solutions using distributed computing (Internship experience does not apply)
- At least 4 years of experience programming with Python, Go, Scala, or Java
- At least 3 years of experience deploying scalable software solutions on cloud platforms (e.g. AWS, Google Cloud, Azure, or equivalent private cloud)
Benefits
- comprehensive, competitive, and inclusive set of health, financial and other benefits that support your total well-being
Job title
Job type
Experience level
Salary
Degree requirement
Tech skills
Location requirements
Lead advanced machine learning model development and optimization at PayPal. Collaborate with teams to deploy scalable ML solutions in production environments.
Senior Machine Learning Engineer at Pivotal Health developing ML systems for healthcare reimbursement. Collaborating across teams to build and maintain reliable, production - grade machine learning systems.
Machine Learning Engineer working with Algorithm team on customer onboarding processes. Focus on execution and automation of models using computer vision and AI in sports industry.
Senior Machine Learning Engineer at Troveo designing and optimizing machine learning pipelines for AI video models. Collaborating with cross - functional teams to build scalable video data solutions.
AI/ML Engineer developing AI systems for data security at Nightfall. Collaborating with engineers and contributing to AI features.
Software Engineer focusing on ML infrastructure for drug discovery at Genesis AI. Leading engineering efforts to enhance scalable platforms for generative modeling and large - scale simulations.
Internship in cheminformatics and machine learning at Genesis Molecular AI, driving drug discovery projects and improving internal tools.
AI/ML Engineer developing machine learning systems for TymeX's digital banking platform. Collaborating across teams to enhance customer interaction and personalization through AI technology.
Lead Machine Learning Engineering at Foundation Health, designing and deploying production ML systems. Collaborate with teams to implement AI/ML solutions for impactful healthcare.