Top Machine Learning Jobs in Buffalo, NY

Own machine learning models end to end, from data preparation and training through evaluation and production deployment. Build pipelines for large multimodal datasets including video, sensor streams, and language. Collaborate with engineering and computer vision teams, rapidly prototype solutions, apply relevant research, and continuously improve production models and tooling.

Source, develop, and close enterprise health system clients across a US territory. Build relationships with healthcare executives, launch and expand pilot programs, create account plans, meet revenue targets, and transition customers to Client Success. Collaborate with product, legal, marketing, support, and leadership; provide sales forecasts and pipeline reporting in HubSpot; influence the product roadmap; and represent Nabla at industry events.

Develop and launch scalable, user-facing features for AI-powered video and multimedia tools. Architect end-to-end solutions across backend and frontend systems, ensure quality and reliability, collaborate with design, product, and ML teams, and mentor engineers. The role involves rapid prototyping, experimentation, technical leadership, and shaping Pika’s core creative product.

Conduct applied AI and machine learning research for semiconductor testing. Define ambiguous technical problems, develop measurable evaluation methods, build and fine-tune models, and create RAG, knowledge-graph, and agent systems. Translate unstructured tester documentation and protocols into production solutions, collaborate with engineering and domain experts, and deploy tools that improve workflows. The role also requires monitoring emerging AI technologies and proposing new research directions.

Lead the technical direction and hands-on development of ML and operations research systems powering delivery dispatch. Build pricing, quote selection, forecasting, routing, batching, fleet recommendation, LLM, optimization, evaluation, and MLOps capabilities. Translate operational constraints into reliable models and solver formulations, establish production ML standards, and mentor engineers. The role partners with Product and leadership to deliver measurable improvements in revenue, margin, utilization, conversion, and delivery reliability.

Lead teams building computer vision and machine learning for AI dash cameras, managing model lifecycle, coordinating with cross-functional teams, and driving AI product development.

Lead Tilt’s product design function across consumer financial products. Set design vision and quality standards, manage and grow a team of six designers and one researcher, build research capabilities, influence product strategy, scale the design system, advocate for users, and partner with Product, Engineering, Marketing, and executive leadership. Drive AI-enabled design practices and ensure product experiences deliver measurable user and business impact.

Manage strategic enterprise accounts, building executive relationships, developing multi-year account plans, driving retention, expansion, upsell and cross-sell opportunities, and closing complex technology deals. Coordinate cross-functional Basis teams, lead executive business reviews, align customer priorities with company objectives, and represent Basis at customer and industry events.

Serve as a customer-facing fraud and identity expert across pre-sales, onboarding, and expansion. Evaluate customer risk profiles, design fraud strategies and identity workflows, advise on risk mitigation and verification optimization, and coordinate technical, regulatory, and operational launch readiness. Lead implementation milestones, go-live planning, and cross-functional resolution of deployment bottlenecks while helping enterprise customers maximize adoption and long-term value.

Own the strategy, roadmap, and growth of Ottimate’s new product initiatives across procurement, budgeting, spend planning, and contract management. Lead customer discovery, validation, MVP development, launches, and portfolio metrics. Manage product managers and designers, oversee Design and UX, and partner with engineering, data science, sales, marketing, and customer success to deliver coherent, market-ready products.

Design, train, and deploy scalable ML models (generative, classification, regression). Define feature roadmaps, collaborate with engineering to implement code and APIs, adapt ML methods for distributed/GPU environments, and create evaluation/annotation programs for fine-tuning and performance measurement.

Owns and grows strategic Financial Services accounts by prospecting, developing channel and alliance opportunities, presenting cybersecurity solutions, closing new business, and expanding customer relationships. The role requires enterprise technology and security sales expertise, executive engagement, account planning, forecasting, reseller collaboration, and the ability to explain NDR, Agentic SOC, AI security, and network visibility solutions. Travel is primarily remote and on the road, with at least 50% travel required.

Leads technical support for VA healthcare AI citizen developers, developing and validating supervised, unsupervised, and reinforcement learning models. Builds synthetic healthcare data cohorts, designs secure cloud-based data science workflows, collaborates with federal stakeholders, interprets analytical results, mentors developers, and ensures model quality, documentation, privacy, governance, and regulatory compliance.

Build and scale production AI systems for Prophetic’s real estate intelligence platform. Responsibilities include developing agentic systems, data pipelines, inference workflows, evaluation benchmarks, geospatial processing, and machine learning applications across hundreds of millions of records. The role requires shipping reliable Python services, optimizing throughput and cost, integrating research into products, and collaborating with engineering and product teams.

Supports enterprise sales by serving as the technical and product expert for Wavelytics, an AI-powered analytics suite. Responsibilities include delivering technical demonstrations, positioning analytics and AI capabilities, translating complex solutions into business value, addressing security and governance questions, supporting customer adoption, creating sales playbooks, and collaborating with Product and Marketing to improve roadmap alignment, retention, and revenue growth.

Design, build, and operate production agentic AI systems for enterprise clients. Develop autonomous agents using Glean, OpenAI/GPT Enterprise, and AWS or GCP. Own agent workflows from architecture and implementation through deployment, monitoring, evaluation, security, and iteration. Apply tool calling, RAG, multi-step orchestration, memory, prompt engineering, structured outputs, permission-aware retrieval, and cost and latency optimization. Measure reliability and quality using evaluations and maintain enterprise-scale production software.

Design, build, and deploy autonomous AI agents using LLMs and agent frameworks; integrate agents into e-commerce and supply chain systems; implement multi-agent orchestration, memory and tool integrations; fine-tune models and apply RL concepts; and build safety, monitoring, and MLOps pipelines.

Design, train, and deploy ML models for Qur'ān recitation, tajwīd detection, and ASR. Build data pipelines for cleaning, segmentation, labeling, and evaluation. Integrate real-time predictions via APIs or lightweight on-device inference, optimize models for speed and mobile performance, and collaborate with backend developers to ship AI features in the app.

Designs integration architecture for third-party agentic AI tools within secure, vendor-isolated government enclaves. Defines data packages, identity federation, tenant isolation, audit logging, and PHI/PII governance. Provides hands-on expertise in LLMs, RAG, fine-tuning, tool use, multi-agent orchestration, and LLMOps. Guides AWS and Databricks-based AI/ML deployments, evaluation, monitoring, and automation while presenting architectures to technical and non-technical stakeholders.

Designs and delivers end-to-end AI/ML solutions on Google Cloud, including data ingestion, feature engineering, model training, deployment, and monitoring. The role leads client discovery, translates business needs into AI/ML strategies, develops roadmaps, provides technical leadership, and supports testing and user training. It requires hands-on Gemini and GCP expertise, customer-facing consulting experience, and collaboration across engineering and product teams.

Lead development and deployment of predictive and ML models for credit risk, collections, and operations. Mentor data scientists, promote MLOps and modern tools, present insights to stakeholders, and drive experimentation with techniques such as causal inference and GenAI.

Develop and deploy machine learning models and production software for multi-object tracking in autonomous trucks. Responsibilities include sensor fusion across camera, LiDAR, and radar; scalable PyTorch training workflows; tracking algorithm development; large-scale data analysis; C++ and Python engineering; safety and validation strategy; architecture decisions; and cross-functional collaboration. The role also provides technical leadership through design reviews, code reviews, mentoring, and engineering best practices.

Own end-to-end analytical lifecycle to develop and optimize business, risk, and growth strategies. Monitor portfolio and macro trends, build and interpret ML-driven models, lead cross-functional launches, mentor junior analysts, and build AI-assisted workflows to improve decisioning and measurement across products and geographies.

Develop agentic AI systems and machine learning methods for drug discovery, including molecular and tabular in-context learning, few-shot adaptation, foundation models, and biological data analysis. Lead research projects from task definition and dataset construction through model training, benchmarking, ablations, and scientific validation. Collaborate with chemists and biologists, build reproducible research software, scale training pipelines, and translate successful methods into drug discovery capabilities and scientific publications.

Supports federal, state, local, tribal, and territorial emergency management planning through geospatial and data analytics. Develops emergency operations, hazard mitigation, and continuity plans; applies FEMA planning processes; facilitates stakeholder coordination; supports exercises, disaster activations, and deployments. Performs quantitative and qualitative analysis, develops database and modeling solutions, applies AI and machine learning techniques, and ensures compliance with government regulations. This is an on-call, non-full-time role requiring frequent travel and extended deployments.