Top AI & Machine Learning Jobs

Manage the AI opportunity roadmap for Risk Services, prioritizing use cases, coordinating internal data science teams and external vendors, translating business needs into requirements, and supporting AI governance, compliance, explainability, auditability, implementation, adoption, and benefits realization. The role focuses on business outcomes and solution delivery rather than hands-on model development.

Lead enterprise AI and digitalization initiatives across Global Operations: identify high-value AI opportunities, design transformation roadmaps and future-state operating models, deliver GenAI/LLM/agentic AI and automation solutions, drive adoption and value realization, and influence senior stakeholders while building governance and capability in a complex, regulated investment-management environment.

Design and own end-to-end computer vision and machine learning pipelines for deployed robotic fleets, covering data collection, training, sensor fusion, evaluation, edge deployment, and telemetry. Develop data engines with auto-labeling, synthetic data, and observability loops; integrate perception with navigation and planning through ROS 2. Evaluate emerging AI methods against latency, cost, determinism, and safety constraints. Mentor engineers, collaborate across hardware and software teams, and occasionally advise clients.

Lead and grow the ML Platform team, owning roadmap, execution, architecture, and production quality for model training, evaluation, serving, and monitoring systems. Partner with customers, Sales, Solutions Engineering, Data Science, Infrastructure, Product, and Customer Success on technical proof-of-value engagements. Modernize feature infrastructure, automate ML lifecycle processes, reduce technical debt, improve evaluation frameworks, and hire and mentor engineering talent.

Develops advanced computer vision and image-processing algorithms for space situational awareness and astronomy applications. Responsibilities include researching SDA/SSA and photometry techniques, training and customizing machine-learning models, managing image-processing pipelines from sensor readout through cloud publication, generating simulated data, validating algorithms, and leading multidisciplinary research projects. The role also requires communicating technical findings to technical and non-technical stakeholders.

Leads the application of AI across business workflows by identifying high-value use cases, advising teams on approved AI platforms and Microsoft 365 capabilities, and translating technical possibilities into business guidance. Partners with security, governance, legal, risk, and compliance stakeholders to address data access, permissions, and operational risks. Oversees solution evaluation, documentation, governance review, rollout, ownership, feedback collection, reusable patterns, and AI adoption best practices.

Research and optimize AI and machine-learning systems across desktop and mobile platforms. Evaluate model performance, develop and deploy on-device applications and agents, and improve browser ML products. Apply systems and network expertise, collaborate with privacy and security researchers, and investigate AI risks. The role involves open-source development, performance benchmarking, and use of modern ML frameworks to deliver privacy-focused solutions.

Lead enterprise AI initiatives as a principal engineer and solution architect. Design, prototype, and deploy scalable generative AI, RAG, agentic AI, and predictive solutions. Translate business needs into technical requirements, evaluate technologies and vendors, conduct architecture reviews, and establish AI engineering, trust, safety, monitoring, and compliance standards. Mentor engineers and guide responsible AI practices across customer-facing products and internal operations.

Lead the data strategy and own end-to-end data pipelines for AI training, simulation, and deployment in safety-critical autonomous systems. Build data flywheels, integrate with large-scale GPU/TPU training infrastructure, ensure simulator fidelity, partner with safety and certification teams, mentor engineers, and track KPIs for pipeline health and model readiness.

Research, develop, and refine predictive signals and quantitative models for systematic investment strategies. Responsibilities include sourcing novel datasets, statistical modeling, machine learning, large-scale data analysis, designing and validating trading signals, backtesting hypotheses across market conditions, and collaborating with portfolio managers to deploy successful ideas.