Top AI & Machine Learning Jobs

Develop mathematical models of complex biomechanical relationships among the knee, hip, and spine. Apply machine learning, computational mathematics, computer vision, and advanced imaging analysis to identify signals and understand dynamic forces. Collaborate with the Spine Service on research focused on musculoskeletal modeling and imaging-based insights.

Designs, builds, deploys, and leads enterprise generative AI solutions, including LLM applications, chatbots, copilots, RAG pipelines, and automation assistants. Integrates AI with Azure, Databricks, EHR, FHIR, and HL7 data while meeting HIPAA, PHI, and governance requirements. Develops data pipelines, evaluates and fine-tunes models, guides architecture decisions, conducts code reviews, mentors team members, and collaborates with clinical, operational, data, and product stakeholders.

Lead Saxo’s global AI and client-facing platform strategy, connecting technology, client experience, commercial growth, operational performance, and regulatory compliance. Own the Platforms & AI journey, define AI integration across Saxo platforms and external ecosystems, shape investment priorities with executive leadership, and lead a high-performing international organization. The role requires strategic vision, strong commercial judgment, executive stakeholder management, transformation leadership, and compelling communication in a regulated financial-services environment.

Build and optimize AI agents and automations across Flosum’s revenue engine. Responsibilities include deploying AI SDR systems, automating lead qualification and CRM workflows, integrating Salesforce with GTM platforms, creating competitive intelligence and personalization systems, improving pipeline conversion, predicting customer churn, supporting product-led growth, and establishing feedback loops for continuous agent refinement. The role requires hands-on coding, AI orchestration, GTM systems expertise, and close collaboration with the CEO and revenue teams.

Leads the design, development, optimization, and deployment of AI models and software applications. Develops APIs using Python and C#, manages complex AI projects, optimizes database queries, ensures data integrity, and collaborates with cross-functional teams. Responsibilities also include technical documentation, code reviews, bug fixes, mentoring junior engineers, and delivering AI solutions that meet business and performance requirements.

Build and operate production-grade AI data pipelines and financial knowledge services for equities content. Responsibilities include processing text, tables, documents, and multimedia; creating knowledge bases and search indexes; engineering RAG retrieval and citation systems; integrating LLMs and NLP models; managing orchestration, APIs, quality, cost, observability, reliability, and scalable production operations.

Designs and develops production AI software across the full software and machine learning lifecycle. Responsibilities include requirements analysis, model and framework selection, AI architecture, RAG implementation, model training and fine-tuning, agent context and memory management, vector database integration, reinforcement learning, documentation, deployment, and technical oversight. The role also tracks schedules and budgets and communicates with customers to align AI capabilities with operational needs.

Design, deploy, and operate distributed backend systems supporting large-scale, low-latency machine learning inference. Lead inference platform direction, optimize high-throughput serving infrastructure, collaborate with ML engineers, and ensure production reliability through monitoring and observability. Manage GCP cloud infrastructure and Kubernetes workloads while applying DevOps, SRE, testing, deployment, and security best practices.

Leads a globally distributed engineering team building production-grade Generative AI products. Responsibilities include GenAI architecture, software development, LLM and RAG solutions, fine-tuning, evaluation, monitoring, security, cloud deployment, technical strategy, stakeholder communication, mentoring, and performance management.

Analyzes internal and external marketing, sales, customer, and product data to develop predictive models, forecasts, dashboards, and actionable business insights. Builds data pipelines and BigQuery queries, performs data preparation and experimentation, and communicates findings through visualization tools. Guides generative AI adoption, develops AI agents and workflow automations, supports data governance, and partners with cross-functional teams to improve decision-making and marketing operations.