Top Machine Learning Jobs in Buffalo, NY

As a Lead Machine Learning Engineer at Root, you'll build scalable ML systems, partner with data scientists, and improve operational practices for customer lifetime value modeling.

Lead the engineering organization for data and AI platforms, making architectural decisions and driving product vision while managing teams and aligning with business priorities.

The Senior AI Engineer will develop AI solutions for incident management, collaborate cross-functionally, and enhance observability tools through iteration and ownership.

The role involves applying established ML techniques to cybersecurity data problems, integrating ML outputs into data pipelines, and ensuring model reliability. Collaboration with engineers and product teams is key, along with strong communication skills and understanding of data flow.

The role involves leading ML client engagements, shaping strategies, advising on ML architecture, and influencing project outcomes, while guiding teams and driving business value.

As a Senior Machine Learning Engineer, you will develop and deploy ML models for health data analysis, collaborating with cross-functional teams to ensure reliability and regulatory compliance.

Design and implement ML infrastructure for scalable deployment, collaborate across teams, optimize ML systems, and enhance tooling.

As a Machine Learning Engineer, you will design, build, and deploy ML models for underwriting at Stripe Capital, ensuring performance while collaborating with multiple teams.

The role involves designing, training, and evaluating ML models, optimizing performance, and collaborating with teams to implement AI technologies in products.

The Machine Learning Systems Engineer will optimize ML training systems, improve data pipelines, and develop GPU kernels to enhance scalability and performance.

The AI/ML Engineering Manager leads a team of ML engineers and architects, managing hiring, performance, and customer engagements while providing technical guidance in ML operations and architecture.

Develop and deploy production-grade AI/ML models, design end-to-end pipelines, and ensure compliance while collaborating with stakeholders in a federal environment.

Lead a team of data scientists developing ML-driven products, managing infrastructure, training models, and ensuring production quality while collaborating with senior leadership.

As an Applied Machine Learning Platform Engineer, you'll design and maintain training infrastructure, manage distributed pipelines, and optimize data workflows for machine learning models.

As a Senior Machine Learning Engineer, you'll design and develop infrastructure and models for AI-powered applications while collaborating with teams to enhance product functionality.

The VP of Research, Machine Learning will set research direction for AI systems, focusing on context representation, reasoning, and evaluation frameworks while ensuring product safety and alignment.

Responsible for building and maintaining end-to-end ML pipelines and deploying production-grade ML systems. Tasks include model fine-tuning, evaluation, and collaboration with engineering teams.

Lead computer vision projects and develop ML models for power grid analysis while mentoring engineers and ensuring software quality standards.

The AI Engineer will build data pipelines and improve ML systems for reliability in production. Responsibilities include schema design, data infrastructure, and model evaluation, ensuring high-quality outputs and integration with product workflows.

Lead design, implementation, and productionization of the platform's ML memory layer and LLM strategies. Build and operate training, fine-tuning, inference, monitoring, and deployment pipelines; collaborate cross-functionally to ensure scalable, reliable ML systems that improve with data and integrate deeply into product workflows.

The AI Director will oversee ML bot development and implementation, identify AI opportunities, educate teams, and prototype AI solutions.