Top AI & Machine Learning Jobs

Own Arcade’s end-to-end machine learning lifecycle, including training pipelines, model development, evaluation, production telemetry, and releases. Build compact, efficient models for tool selection, routing, retrieval, recommendations, and agent memory. Productionize models for customer VPCs and air-gapped environments through quantization, optimization, packaging, and serving. Set the ML strategy and stack, establish evaluation practices, convert agent traces into training data, and help grow the ML team.

Supports the design, development, and governance of AI and ML architectures for enterprise modernization. Responsibilities include building cloud data pipelines, AI governance frameworks, agentic AI and RAG architectures, vector database integrations, model monitoring, explainability, bias detection, drift management, and architecture documentation. Collaborates with enterprise architects and engineering teams while supporting governance reviews and technical assessments.

Build and deploy AI workflows using AIVA, designing prompts, tool integrations, evaluation frameworks, guardrails, exception handling, and human-in-the-loop patterns. Integrate AI solutions with APIs, databases, and enterprise systems, then rapidly test and refine solutions with clients. Collaborate with technical leads, business analysts, integration engineers, and operations subject matter experts to deliver production-quality AI applications.

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.

Develops and evaluates machine learning models for United States Air Force mission problems. Responsibilities include data preparation, feature engineering, statistical analysis, model training and validation, performance monitoring, data governance, DevSecOps, compliance documentation, and dashboard development. Collaborates with government stakeholders, engineers, cybersecurity personnel, architects, and mission users while maintaining auditable model lifecycle records and adhering to TS/SCI security requirements.

Leads the technical direction and implementation of AI systems for global supply chain applications. Builds production-grade solutions across data pipelines, machine learning, LLMs, agentic AI, software architecture, deployment, and monitoring. Translates AI research into enterprise products, integrates legacy systems and cloud infrastructure, ensures responsible AI practices, and mentors engineers while guiding technical initiatives in a fast-paced startup environment.

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.