Top AI & Machine Learning Jobs

Analyze large-scale user and app datasets to generate actionable insights for advertising campaign performance and product strategy. Partner with business and technical teams on rigorous analysis, testing, and opportunity identification. Communate findings and recommendations to stakeholders, while using market and performance data to influence product and machine learning roadmaps. The role requires strong statistical reasoning, hypothesis testing, Python, SQL, and data analysis capabilities.

Deploy AI and robotics software at the tactical edge by adapting vision and language models for efficient local operation. Build reliable systems using Python, machine learning frameworks, containerization, model-serving tools, and vector databases. Work directly with customers and operational teams to rapidly develop, test, deploy, and troubleshoot solutions in challenging field environments. The role is office-based in Syracuse with travel of up to 30%.

Design, develop, and deploy AI solutions that improve senior healthcare delivery, automate scheduling and care-management workflows, integrate with EHR and resident-management systems, and support clinical decision-making. The role involves healthcare data analysis, LLM and agentic application development, model monitoring and optimization, regulatory compliance, and collaboration with clinicians and operations teams. Strong skills in Python, machine learning frameworks, data engineering, Azure ML, and healthcare data security are required.

Build and operate production AI systems, including LLM-backed APIs, agents, retrieval and RAG workflows, evaluation systems, ingestion pipelines, and data services. Develop reliable data paths connecting customer environments, cloud storage, and compute clusters. Partner with Product and Design to define requirements, make architectural decisions, ship platform improvements, and own post-launch performance across backend services, cloud infrastructure, and occasional UI work.

Leads firmwide AI, automation, and digital innovation strategy; identifies and prioritizes AI-enabled business solutions; establishes responsible AI governance; drives technology adoption, change management, training, and stakeholder engagement; partners with IT to implement secure solutions; and measures adoption, productivity, operational impact, and business value across technical and non-technical teams.

Leads New York State initiatives strengthening quantum and AI innovation ecosystems. Oversees the Quantum Hubs program, develops statewide strategies, tracks impacts, identifies federal funding opportunities, builds partnerships across industry and academia, coordinates government stakeholders, supports grant proposals and communications, and advances research commercialization across quantum computing, communications, cryptography, sensing, AI, semiconductors, optics, and photonics. Travel up to 25% is required, primarily within New York State.

Build and deploy production machine learning and AI solutions on Databricks and Azure. Responsibilities include data discovery, feature engineering, model development, experiment tracking, model serving, monitoring, retraining, LLM and RAG use cases, responsible AI documentation, and modernization of legacy R, Stata, and SAS workloads. The role also involves client consultation, cloud adoption, training, mentoring, and communicating results to technical and executive audiences.

Build and scale AI-powered financial tools using LLMs, generative AI, heuristics, document extraction, transaction tagging, embeddings, and vector search. Improve evaluation pipelines, prompt tooling, AI infrastructure, and human-in-the-loop systems for bookkeeping and financial workflows. Collaborate with product, design, engineering, and operations to deliver reliable AI experiences using messy real-world data.

Conduct frontier AI detection research under mentorship, selecting a topic such as adversarial robustness, interpretability, multimodal detection, agentic AI detection, or AI-generated training data. Interns are expected to develop research suitable for publication at a major AI conference, train neural networks, and contribute to novel AI detection methods.

Leads strategy, roadmap, discovery, development, and launch of AI-native products across multiple client engagements. Owns product requirements, prioritization, release planning, client relationships, presales scoping, solution design, and executive communication. Partners with engineering, design, data science, and delivery teams to ship scalable products. Requires hands-on experience with production LLM and agentic AI products, prompting, context design, evaluation, Agile delivery, and technical product specifications.