Top Remote Machine Learning Jobs in Buffalo, NY

The VP of AI will lead NeoWork's AI strategy, ensuring adoption, developing workflows, building capabilities, and enhancing client value through AI integration.

The role involves defining the vision and strategy of the Outreach AI Platform, prioritizing the roadmap, collaborating with engineering and data science teams, and engaging with customers to refine AI strategies.

Design and implement machine learning models to solve business problems, analyze data for insights, and optimize model performance.

As the Lead AI Strategist, design and deploy advanced AI/ML models to optimize revenue and mitigate medical costs, impacting financial performance directly.

Lead Reliability & Observability initiatives, collaborate with engineering and product teams, ensure robust operations of Airbnb's platform, and drive AI projects.

This role involves leading a perception team in developing algorithms for autonomous driving systems, supervising team members, and conducting research to enhance perception capabilities.

The Director of Artificial Intelligence will define, lead, and implement AI strategies, managing R&D, building teams, and ensuring ethical AI use to drive business outcomes.

As a Product Manager for Partnerships, you'll drive execution of partnerships by defining requirements, prioritizing tasks, and ensuring high-quality integration of partner-driven initiatives. You'll work across business development, engineering, and operations, managing multiple integrations effectively, and may require up to 30% travel for collaboration with partners and teams.

Manage and mentor engineers, contribute to product and infrastructure, drive projects, and collaborate cross-functionally to enhance AI platforms.

Lead the implementation of AI-driven supply chain solutions, collaborating with clients to enhance their business processes and ensure seamless technology integration.

Drive new enterprise customer acquisition and growth for Arize AI by prospecting, conducting discovery with AI/ML stakeholders, running POCs, and closing full-cycle SaaS deals. Build relationships, create sales plays, scope licensing, and collaborate internally to drive adoption and expansion.

Lead a team of machine learning data scientists, overseeing model development for fraud detection and risk assessment, and ensuring quality throughout the model lifecycle while mentoring the team.

The Senior Machine Learning Engineer will deploy and maintain ML models, collaborate with cross-functional teams, and optimize ML system performance in a fully remote role.

As an MLOps/ML Platform Engineer, you will build and manage ML systems, optimize workloads, and ensure production model reliability while collaborating across teams.

The Machine Learning Engineer will develop and optimize models for processing tax documents, mentor juniors, and collaborate across teams.

The MLOps Engineer will design scalable ML infrastructure, manage containerized environments, develop training pipelines, and implement CI/CD pipelines, ensuring model reliability and performance.

Design, build, and scale ML and Generative AI systems. Develop applications using ML architectures and monitor model performance.

As a Software Engineer, AI/ML GenAI, you will design and implement AI features, manage embeddings, and optimize AI systems for production deployment while collaborating with product and design teams.

The Staff Machine Learning Engineer will lead ML initiatives, develop AI systems, optimize pipelines, and mentor team members to automate healthcare processes.

The Applied Scientist AI/ML Intern will work on building data pipelines, finetuning LLMs, and deploying AI models in a collaborative team environment.

Lead the design and operation of Twilio's ML and data platform, architecting cloud-native pipelines and implementing MLOps best practices while mentoring engineers and collaborating across teams.

The Machine Learning Engineer will execute the full ML lifecycle, collaborating on model implementation and improving customer experiences through data-driven insights.

Design, implement, and optimize scaled lifecycle campaigns across email and in-product channels to drive activation, revenue, retention, and cross-sell. Use marketing automation, behavioral data, and ML techniques to personalize experiences, analyze performance, and collaborate across growth, product, sales, and data teams.

The role involves researching, developing, and implementing AI solutions, optimizing models, integrating AI into workflows, and collaborating with teams to enhance business efficiency.

The role requires a skilled Machine Learning Engineer to create complex models and AI solutions while collaborating with teams to enhance products and services.