Top AI & Machine Learning Jobs

Architect and implement enterprise-scale AI systems for financial and alternative data, including multimodal ML pipelines, NLP, LLMs, computer vision, deep learning, and conversational AI. Lead MLOps, model deployment, observability, RAG, semantic search, and continuous learning initiatives. Collaborate with product and research teams, establish engineering standards, mentor engineers, and advance AI-driven financial analytics solutions.

Design, productionize, and scale machine learning and optimization models, pipelines, libraries, and deployment platforms. Partner with data scientists, engineers, product managers, and business stakeholders to deliver ML capabilities, personalization systems, and reusable end-to-end workflows. Establish ML engineering best practices, troubleshoot data pipelines, mentor junior professionals, and help define BetterHelp’s ML platform strategy.

Designs, develops, and maintains backend services, APIs, data structures, and architecture for conversational AI and automation applications. Integrates systems such as scheduling platforms and EHRs, implements authentication and HIPAA-appropriate security controls, and protects PHI and PII. Optimizes performance, scalability, reliability, and stability while troubleshooting defects. Collaborates with product, QA, integration, frontend, and design teams to deliver secure, reusable, well-tested solutions.

Supports AI and machine learning modernization projects by identifying integration opportunities, developing and testing AI-assisted tools, researching frameworks and APIs, collaborating with scientists and IT teams, and documenting workflows and outcomes. The intern will help automate internal business and laboratory processes and must work onsite in the Pittsburgh metro area during Fall 2026.

Build and operate an enterprise AI governance program: author the AI-SSDLC, gate agent deployments, lead threat modeling/red teams, monitor AI behavior and compliance, align with Legal/Privacy/Compliance, and implement automation and dashboards for executive reporting.

Assist cloud and generative AI teams in designing, building, testing, and deploying AI agents, agentic workflows, AI-powered web applications, RAG pipelines, and enterprise LLM integrations. The intern will support prompt engineering, evaluate AI outputs, write documented Python code, participate in reviews, gather requirements, and collaborate with cross-functional teams in a hybrid environment.

Supports the AI, Data and Digital Transformation team through the data science lifecycle, including analyzing large datasets, identifying trends, developing and evaluating predictive and machine learning models, and presenting project findings and business impact. The intern will contribute to analytics and digital transformation initiatives, collaborate with team members, and gain project management, teamwork, and leadership exposure.

Build the core agentic platform for global trade, including tool routing, policy enforcement, memory, context graphs, evaluation infrastructure, APIs, and scalable production systems. The role focuses on making AI agents reliable enough to execute financial and operational actions using messy enterprise data. Responsibilities include product ownership, customer collaboration, system design, production delivery, and potentially recruiting and mentoring engineers.

Build and operate custom AI agents for private-market clients end to end, from discovery and requirements through deployment, rollout, adoption, and ongoing iteration. Design agentic systems with retrieval, context design, tool and MCP integrations, orchestration, evaluation, observability, and cost controls. Work directly with clients, make product and engineering trade-offs, manage cloud infrastructure, document solutions, train users, and mentor less experienced engineers.

Develops AI and machine learning healthcare products, including NLP models for unstructured medical records and classification models for clinical programs. Responsibilities include designing, training, deploying, and evaluating ML and deep learning architectures; conducting statistical analysis, exploratory data analysis, and visualization; resolving data issues; delivering analytic insights; and supporting governance, reporting, and process improvements.