Top Remote Machine Learning Jobs in Buffalo, NY

As a Senior Technical Product Manager, you will lead the AI Platform team to define roadmaps, collaborate with technical experts, and improve healthcare delivery by leveraging AI technologies.

The Global Process Owner for S&OP leads the design and continuous improvement of the S&OP process, ensures collaboration across functions, and drives digital transformation and automation within the organization.

Senior Business Systems Analyst to define requirements for agent-facing servicing applications, design integrations/APIs with core banking systems, translate needs into user stories and acceptance criteria, improve agent workflows and productivity, and identify AI/ML opportunities for fraud detection, automation, and operational analytics. Supports testing, validation, and releases.

Lead cross-functional technical programs, coordinate launches, develop scalable processes and KPIs, manage risks and intake prioritization, influence product and technical strategy, and drive timely delivery with engineering and business stakeholders.

Gather and document functional requirements for agent-facing servicing applications, define integrations/APIs with core banking systems, consult with stakeholders, identify AI/ML opportunities (fraud, KYC, automation), support testing and implementation, and act as SME while managing risks and compliance.

Lead cross-functional technical programs from planning through delivery. Coordinate engineering, architecture, and business teams to launch capabilities, manage risks, prioritize intake, define KPIs and processes, influence technical strategy, and ensure timely delivery of large customer-facing initiatives.

Design, build, and deploy conversational agents using Microsoft Copilot Studio. Gather stakeholder requirements, implement NLU/NLP, integrate with enterprise systems, test and refine agents, and provide documentation and user support for HR, policy retrieval, and procure-to-pay automations.

Lead design, implementation, and scaling of enterprise Palantir data solutions. Oversee cross-functional teams to build data pipelines, integrations, governance, and advanced analytics (ML/AI). Manage project delivery, stakeholder engagement, performance optimization, and mentor senior engineers and analysts while ensuring security and regulatory compliance.

Lead digital transformation engagements by developing strategy, advising executives, and delivering end-to-end projects. Drive adoption of cloud, AI, data analytics, and emerging technologies while managing multidisciplinary teams, ensuring compliance, and measuring outcomes through KPIs.

Own a portfolio of large manufacturing accounts, drive net-new pipeline and full-cycle enterprise deals, build C-suite relationships, translate AI/vision capabilities into ROI, execute land-and-expand strategies, and travel 40-50% for on-site engagement and executive reviews.

Design and implement conversational AI solutions using NLP, ML, and LLMs. Build ETL pipelines and integrate relational and NoSQL databases. Develop Voice AI interactions on Vertex AI, work with Nuance IVR and Microsoft platforms, and write production-grade Python code.

Design, build, and deploy conversational AI solutions on the Kore.AI platform. Create and configure chatbots (intents, entities, dialog flows), integrate with back-end systems and REST APIs, and develop responsive user interfaces using ReactJS alongside UI/UX designers.

Lead enterprise design and deployment of a global operating model for bovine IVF production. Drive standardization, SPC, automation, AI/analytics, Lean/Six Sigma, quality systems, and continuous improvement to scale consistent, high-throughput production across labs and field operations.

Lead a team of analysts to advance fraud and safety operations through analytics, ensuring robust data products and compliance while empowering stakeholders with actionable insights.

Lead go-to-market strategy for Magnet Forensics' security, incident response, and AI product portfolio. Influence executive, product, and sales stakeholders; translate technical AI/ML and forensic capabilities into messaging; develop buyer personas; create sales enablement and demand-generation content; evangelize products at events; partner with competitive intelligence and customer advocacy; mentor product marketing team members.

The Software Engineering Manager will lead a team in creating tools for Cengage Learning Platforms, focusing on AI innovation, team growth, agile delivery, and collaboration.

Design reusable software tools and subsystems, lead implementation and estimation, ensure systems integration and multi-platform compatibility, drive projects to completion, and perform feasibility analyses for future projects.

Lead architecture and engineering for AI-powered applications across backend, mobile, and desktop. Own reliability, observability, APIs, and model integrations. Grow and mentor a small senior applications team, drive production readiness, and make high-impact tradeoffs across latency, cost, security, and user experience to deliver reliable, low-latency AI-enabled workflows.

Transfyr seeks adaptable builders for various roles across CS, biology, and business. Responsibilities include developing sensor stacks, data harmonization, computer vision, machine learning systems, lab operations, project management, and business development.

Own the technical side of deal progression for airline deployments: qualify opportunities, run demos and POCs, author integration and deployment architectures (PSS, APIs, data feeds, DNS, identity, networking), respond to RFPs/security questionnaires, scope SOWs and NFRs, and build reusable demo/reference assets to drive adoption and expansion while liaising between sales, engineering, and customers.

Design, develop, and implement AI/ML models; analyze large datasets; integrate AI solutions into existing systems; apply MavLink for autonomous systems; collaborate across teams and improve ML development processes.

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 the development of production-grade machine learning systems, build ML pipelines, and optimize models for deployment and performance.

Independently validate and monitor machine learning models for credit underwriting, credit risk, and fraud detection. Identify model weaknesses, recommend improvements, and collaborate with model owners to remediate findings. Support and maintain the Model Risk Management framework and coordinate with Internal Audit, Internal Controls, and Compliance on audit and regulatory requests.

As a Machine Learning Engineer II, you will enhance AI systems for automating customer operations, develop evidence extraction pipelines, and collaborate with teams on model development and deployment.