Top Remote Machine Learning Jobs in Rochester, NY

As a Senior Machine Learning Platform Engineer, you'll architect a scalable ML platform, collaborating with teams to ensure compliance, optimize performance, and guide best practices in ML workflows.

Build and own ML platform infrastructure and tooling to enable model development, deployment, monitoring, and iteration. Lead realtime model monitoring, experiment tracking, reproducibility, and operational reliability to scale fraud-prevention ML systems in production.

Manage AI/ML projects from Proof of Concept to Minimum Viable Product using Agile methodologies while ensuring stakeholder communication and project alignment.

The role involves building and maintaining ML pipelines, optimizing workflows, and contributing to product strategy with a focus on usability.

Design, build, and maintain ELT pipelines and analytics-ready data models to support reporting, KPIs, and downstream ML use cases; implement data quality, optimize pipeline performance, document lineage, and ensure governance and privacy.

Design and maintain datasets, build and evaluate domain-specific ML and vision-language models for real-time privileged access threat detection; deploy and optimize Python/Docker inference services integrated with WebSocket/WebRTC and protocol-level interfaces; monitor and document production models.

Lead QA for AI/ML features: design and execute test plans, build Python automation to validate LLM outputs, guardrails, and data pipelines; perform regression, security/privacy validation, and analyze logs/metrics to ensure reliability.

The Senior ML Engineer will develop and optimize machine learning solutions for audience intelligence, build production pipelines, and collaborate with cross-functional teams to enhance user engagement and revenue.

Design and build machine learning models for Zillow's product features, optimize AI components, and mentor engineers within a cross-functional team.

Own and deliver the financial and corporate systems product roadmap for billing and finance. Define end-to-end experiences, lead 1-2 scrum teams, prioritize via qualitative and quantitative insights, collaborate with Finance/HR/Analytics/UX, and drive adoption and financial outcomes through iteration, experimentation, and stakeholder alignment.

The Senior Machine Learning Engineer will build and operate ML systems end-to-end, focusing on production-grade ML platforms, feature pipelines, and MLOps practices while collaborating with cross-functional teams.

Analyze biological datasets using machine learning and deep learning, collaborate on research projects, and communicate findings through visualizations and reports.

The AI/ML Specialist Solution Architect Intern will assist in designing AI solutions, learning about distributed workloads, and preparing technical materials, gaining mentorship and exposure to modern ML frameworks and GPU infrastructure.

The Senior Analyst in Predictive Analytics and Machine Learning develops and maintains statistical models to support business decisions, monitors model performance, and collaborates with stakeholders across the company.

As a Senior ML Engineer for Payments, you will develop AI solutions for payment experiences, optimize transaction processing, and ensure system reliability while mentoring teammates and collaborating with cross-functional teams.

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

Design and implement production-grade ML systems for ICS/OT cybersecurity, build and optimize models (including NLP/LLMs), develop data pipelines and MLOps workflows, collaborate with data teams to deploy and monitor models in cloud and on-prem containerized environments, and troubleshoot production performance and resource issues.

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

As a Senior AI/ML Engineer, you will design and develop advanced AI solutions for finance, focusing on ML and GenAI, ensuring compliance and operational efficiency.

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.

Manage and mentor a team of machine learning and data engineers, overseeing ML model development, data pipelines, and collaborating with product teams on technical requirements.

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.

Develop and deploy AI agents, integrating them with external systems and optimizing performance in multi-agent environments. Collaborate with product managers to align agent capabilities with business needs, exploring technologies in Generative AI and LLM orchestration.

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