Top Machine Learning Jobs in Buffalo, NY

Leads engineering managers, software engineers, architects, data engineers, and support teams responsible for enterprise fraud technology platforms. Oversees real-time fraud detection, transaction monitoring, risk scoring, identity and payments fraud controls, case management, and 24x7 operations. Sets technology strategy, budgets, governance, SLAs, security, resiliency, and modernization standards. Drives cloud-native architecture, CI/CD, Infrastructure as Code, SRE, observability, event-driven systems, and real-time analytics while partnering with risk, cybersecurity, compliance, fraud operations, and business leaders.

Leads engineering strategy, teams, budgets, modernization, and operational accountability for enterprise AML technology platforms. Oversees transaction monitoring, risk scoring, investigations, case management, regulatory reporting, and financial crime detection capabilities. Drives cloud modernization, resiliency, security, CI/CD, Infrastructure as Code, SRE, observability, advanced analytics, machine learning, and graph analytics. Partners with AML Operations, Compliance, Risk, Cybersecurity, Audit, and business leaders while managing vendors, governance, controls, talent development, and platform performance.

Lead enterprise architecture strategy and roadmaps for commercial lending technology at M&T Bank. Design scalable, secure, and resilient solutions; integrate lending platforms, data, and systems; ensure regulatory and risk alignment; govern implementation; evaluate vendors and emerging technologies; and advise senior business and technology leaders. The role supports commercial lending capabilities including origination, underwriting, servicing, portfolio management, treasury, collateral, and customer relationship management.

Lead GitLab’s company-wide strategy for autonomous, agentic software development workflows. Identify and validate AI opportunities across the SDLC, build prototypes and reference architectures, establish evaluation frameworks and safety controls, and drive internal adoption. Partner across Engineering, Product, Infrastructure, Security, Architecture, and Data and ML teams to scale reliable, observable, compliant agentic capabilities into customer-facing products. Mentor senior technical leaders and represent GitLab externally on AI-assisted development and productivity measurement.

Leads high-visibility product programs focused on market adoption, analyst relations, customer advisory boards, executive briefings, and industry events. Translates technical product roadmaps into compelling market value propositions, synthesizes analyst and customer feedback, and provides actionable insights to product leadership. Partners cross-functionally with Product, Marketing, and executive stakeholders while supporting strategic initiatives and external-facing communications.

Build and operate production machine learning systems for Safety AI, including low-latency APIs, data pipelines, model serving, evaluation, monitoring, and rollout infrastructure. Process large-scale camera and telematics data, optimize cloud and edge-to-cloud execution, track model performance and drift, and partner with applied scientists, firmware engineers, platform teams, and product managers to deliver reliable safety features.

The Senior Data Scientist develops quantitative models for financial crime compliance, performs data analysis, and collaborates across teams to enhance model performance.

Own the strategy, architecture, development, deployment, monitoring, and continuous improvement of production personalization and recommendation systems. Build custom embeddings, ranking models, and shared representations across feeds, search, and recommendations. Lead ambiguous problems from concept to measurable customer impact, establish AI evaluation standards, partner with product and data teams, make cross-team technical decisions, and coach senior engineers.

Engineer AI-powered threat detections, automated response playbooks, and continuous adversary emulation. Apply machine learning and LLMs to security operations, conduct threat hunting and AI red teaming, map coverage to MITRE ATT&CK, and develop detection-as-code through CI/CD. Build production-grade Python integrations, measurable containment capabilities, and safe autonomous defenses with guardrails. Mentor security professionals and collaborate across threat response, cyber defense engineering, and platform teams.

Design, build, and operate high-throughput, low-latency ad-serving infrastructure processing millions of requests per second. Optimize GPU-powered TensorRT inference, feature stores, model serving, benchmarking, experimentation tooling, and system reliability. Partner with machine learning engineers to productionize models and own systems through design, deployment, observability, capacity planning, incident response, and optimization.

Leads advanced data science and research initiatives for materials discovery, product development, and manufacturing optimization. Analyzes experimental and manufacturing datasets, develops predictive and physics-informed models, builds scalable data pipelines and databases, establishes data-quality practices, and communicates insights to technical and business stakeholders. The role collaborates with chemists, materials scientists, engineers, and other partners to create validated, scalable analytical solutions for advanced materials applications.

Own end-to-end client relationships for a book of B2B SaaS customers: drive adoption, engagement, retention, and expansion of AI-powered contract drafting tools. Lead onboarding, training, large-scale rollouts, and translate customer feedback into product improvements while designing scalable CS processes and tracking health metrics to demonstrate value.

Provides deep technical sales support for Red Hat AI, helping enterprise customers design, deploy, and adopt AI platforms. Architects Kubernetes-based AI systems, GPU-enabled workloads, data and ML pipelines, and model training and serving environments. Leads technical workshops, proofs of concept, presentations, business-value discussions, and customer advocacy. Collaborates with engineering, sales, consulting, customer success, and partners to validate solutions, provide product feedback, and drive AI adoption.

Support analytics by cleaning, validating, and transforming data; build dashboards and KPI reports; perform trend, cohort, and A/B testing analyses; improve data pipelines and forecasting; present insights to cross-functional teams.

Analyze complex datasets, develop predictive models, identify trends, create data visualizations, validate data quality, and collaborate with business stakeholders to support data-driven growth and decisions.

Design and deploy production-ready LLM pipelines and agentic workflows, automate large-scale multimodal dataset pipelines, monitor and improve agent performance, and integrate AI agents with 3D scene composition. The role also involves process documentation, incident response, and continuous improvement, with collaboration across the engine team and company founders.

Lead end-to-end delivery of strategic data science and AI solutions for AI infrastructure. Work with telemetry and experimentation at fleet scale to build, deploy, and measure predictive and prescriptive models. Partner with stakeholders, present insights, enable adoption, and promote responsible AI. Collaborate closely with data engineers and infrastructure teams to productionize scalable ML solutions.

Develop and deploy LLM and machine learning models for Airbnb Community Support. Responsibilities include automating LLM evaluation, curating synthetic datasets, analyzing customer issues, identifying model failure modes, building personalization models, exploring data, prototyping solutions, and communicating insights to cross-functional stakeholders. The role focuses on scalable, production-ready AI products and operational optimization.

Own end-to-end technical delivery for wellness clients, from pre-sales demonstrations and requirements gathering through production deployment. Build AI agents, insight products, data ingestion and ETL pipelines, and customer-facing software. Design system architectures, integrate complex client data sources, lead technical working sessions, communicate tradeoffs, and influence product direction through customer deployment learnings.

Leads the AI product strategy and roadmap, extending governed Alteryx workflows and business logic into enterprise AI agents and LLM ecosystems. Owns the Faster, Better, Cheaper ROI narrative, benchmarks product performance, quantifies customer value, champions trustworthy AI through the VURA framework, and represents the product vision with customers, analysts, and external audiences.

Own the enterprise architecture vision for scalable, high-performance, cost-efficient multi-tenant SaaS systems. Lead AWS cloud optimization, distributed data and AI infrastructure, architectural governance, and Architecture Review Council activities. Guide engineering teams on microservices, event-driven systems, automation, and cloud-native practices while driving GenAI adoption across the software lifecycle. Build prototypes, establish architectural standards, optimize total cost of ownership, and mentor technical leaders.

Leads Dandelion Health’s AI developer business by building pipeline, closing clinical AI data and validation partnerships, recruiting developers into its marketplace, and achieving revenue targets. Develops commercial strategy, manages executive relationships, structures agreements, and collaborates with marketing, product, data, engineering, and clinical informatics teams. Represents the company at industry events and advises on clinical AI validation, regulatory, payer, and competitive dynamics.

Leads advanced analytics for Tribal healthcare initiatives, including data resource inventories, quality assessment, linkage analysis, predictive and prescriptive modeling, dashboards, and management reporting. Develops data standards, quality frameworks, automation, and infrastructure strategies while ensuring federal compliance. Coordinates cross-functional analytics projects, supports healthcare quality measures, mentors junior staff, and promotes data-driven decision-making using Python, R, SQL, statistical methods, and machine learning.

Researches, develops, tests, and maintains materials-aware machine learning models and software. Designs scalable ML systems, improves model interpretability and uncertainty quantification, and develops inverse-design capabilities. Collaborates with product, engineering, and external research teams to deliver customer-focused AI solutions, publishes research, reviews code, analyzes model performance, and mentors developers.

Leads Humana’s enterprise digital platform portfolio, defining multi-year product strategy, investment priorities, adoption plans, governance, and value-realization metrics. Oversees platforms including Adobe Experience Platform, consent management, digital health, design systems, content, and reusable digital services. Partners with business, design, technology, and engineering leaders to align roadmaps, drive enterprise adoption, manage trade-offs, and improve customer and business outcomes. Develops senior product leaders and cross-functional teams in a regulated environment.