Top Remote Machine Learning Jobs in Rochester, NY

Build and maintain ML and generative AI tooling, training and inference platform services, model evaluation and testing frameworks, and APIs to expose ML products. Deploy and manage applications across the AI/ML SDLC, mentor teammates, and write production-quality code daily.

Build and deploy production machine-learning systems within client environments, covering data discovery, modeling, validation, deployment, scheduling, monitoring, and retraining. Develop forecasting and detection models for imperfect time-series data, establish honest evaluation and KPI frameworks, and ship usable APIs or interfaces. Work directly with client IT and data teams through discovery, demos, and feedback cycles. The role also involves data engineering, software development, basic cloud and DevOps work, and advising clients when ML is not appropriate.

Design, build, and deploy novel sensor hardware and data acquisition systems; curate geoscientific datasets; develop statistical, physics-based, and ML models (2D/3D) to predict mineralization; create visualizations and evaluation tools; collaborate with geoscientists, engineers, and vendors; deploy sensors in the field and integrate results to guide exploration programs.

Lead machine learning initiatives, oversee product development, improve execution pace, enhance team reliability, and foster team growth at Hightouch.

The Director of Product Management will oversee the product lifecycle for AI products, focusing on customer collaboration, rapid prototyping, and product strategy. The role involves leading product teams, driving execution, and shaping the product culture.

Lead perception vision, strategy, and roadmap for AV perception. Drive advances in detection, classification, tracking, fusion, and prediction; design sensor suites; define KPIs; hire and manage engineering teams; coordinate cross-functional deployment and issue resolution to ensure robust commercial autonomy performance.

The Enrollment Advisor helps prospective patients through the onboarding process, providing information and support throughout their enrollment in Revero's program.

The VP of Marketing will lead the marketing ecosystem to drive growth, define competitive advantages, influence product design, and mentor a diverse team towards a mission-driven culture.

As a Senior Staff Machine Learning Engineer, you will drive AI product development, collaborate with cross-functional teams, and enhance ML models at scale.

The SAP Innovation Architect will design SAP automations, mentor engineers, and drive innovation in AI-driven SAP operations, while ensuring scalability and security in integrations.

Act as the technical lead in enterprise sales: run technical discovery, design and deliver customized demos and POCs, assess API/cloud/model fit, address technical objections, support contract negotiations, and relay product feedback to engineering while staying current on AI/ML trends.

Lead backend architecture and implementation for scalable, reliable cloud-hosted web products. Mentor engineers, set standards, integrate agentic AI coding tools, design systems applying ML to large datasets, and balance customer-facing features with technical debt and performance.

Lead the Contracts product roadmap for a B2B healthcare SaaS platform, applying deep reimbursement and managed care expertise. Partner with AI engineers to build contract-understanding models, drive adoption, iterate on workflows, create business cases, and coordinate cross-functionally with Legal, Finance, Sales, and SMEs to ensure compliant, revenue-aligned contract execution.

Lead technical engagements with public sector customers, onboard and train users, analyze infrastructure and workflows, drive migration from on-prem to cloud HPC, collaborate with architects and product teams, and present Rescale solutions to grow customer adoption and improve the ScaleX platform.

Design and execute research for early, noninvasive cancer detection using cfDNA. Lead molecular and clinical data analysis, develop high-throughput NGS bioinformatics pipelines, collaborate with wet lab teams to assess assay performance and iterate on experimental methods, and propose computational improvements for assays and models.

Develops and deploys machine learning systems for high-stakes decisions in pricing, claims, underwriting, and predictive health. Responsibilities span problem framing, quantitative research, experimentation, causal inference, predictive modeling, multimodal and foundation model development, agent systems, production deployment, and ongoing iteration. The role requires strong mathematical and statistical foundations, Python fluency, and expertise in machine learning, statistics, econometrics, or causal inference.

Own end-to-end computer vision and machine learning projects for power grid infrastructure, from problem framing and research experimentation through production deployment and monitoring. Develop detection, segmentation, classification, anomaly detection, and foundation-model solutions; build data pipelines, serving systems, experiment tracking, and model versioning. Conduct error analysis, benchmarking, tuning, code reviews, and testing while translating client requirements into reliable production models and communicating technical decisions and limitations.

Build and improve machine learning systems for ad ranking, relevance, and optimization. Responsibilities include developing active learning and LLM-assisted labeling workflows, owning experimentation and evaluation, and deploying low-latency inference services. The role partners with product, data, and platform teams to improve advertiser performance and user engagement while ensuring production reliability, scalability, latency, and data quality. Candidates will use Python, Go, AWS, distributed systems, and AI-assisted development tools.

Lead the development of deep learning models for email data, optimize models, design evaluation frameworks, integrate products, and mentor junior researchers.

Lead onboarding and training for new customers of an AI-powered construction SaaS. Customize training, run webinars and office hours, create documentation, drive user adoption, surface issues with support, and collaborate with product and customer success to refine onboarding processes.

Lead strategy, roadmap, and delivery for Tekmetric's SaaS product for auto repair shops. Own user research, AI/ML product initiatives, cross-functional alignment, go-to-market coordination, and measurement of business outcomes. Train internal teams and iterate on features from concept through launch and optimization.

Build and deploy low-latency machine learning models and inference infrastructure for Netflix’s advertising platform. Develop identity resolution, behavioral and contextual targeting, lookalike expansion, user signals, and audience features at massive scale. Collaborate with science, product, engineering, operations, design, and research teams to productionize models while supporting privacy, governance, security, and advertiser brand safety.

Leads the architecture, research, development, evaluation, and production deployment of GenAI and LLM-based healthcare ML solutions. Works with healthcare data, cloud platforms, data engineering, and software teams to deliver SaaS-scale systems. Establishes responsible AI practices, designs RAG and NLP solutions, advises clients, leads technical discussions, mentors teams, manages large projects, and translates technical insights into measurable business outcomes.

Lead full sales cycle to drive CTV performance advertising growth in North America: prospect, close, launch and scale CTV budgets, manage key advertiser relationships, collaborate cross-functionally, represent Jampp at events (20% travel), and provide local feedback to improve global initiatives.

Senior Business Systems Analyst responsible for capturing functional requirements for agent-facing servicing applications, defining integrations/APIs with core banking systems, translating needs into user stories and acceptance criteria, supporting testing and releases, analyzing workflows to improve agent productivity, and identifying AI/ML opportunities for fraud detection and automation.