Top AI & Machine Learning Jobs

Conduct deep AI and quantitative research for electronic trading by pre-training Transformer and time-series foundation models on large-scale financial datasets. Develop representations, architectures, self-supervised objectives, distributed training systems, fine-tuning methods, and evaluation protocols for alpha generation, pricing, execution, market making, and risk management. Study scaling laws, transfer learning, robustness, data efficiency, inference cost, and latency while collaborating with infrastructure engineers to build reusable model-training and serving components.

Lead development and operation of scalable machine learning training platforms across AWS and Kubernetes. Optimize distributed GPU workloads, training performance, reproducibility, cost, and observability. Build CI/CD automation, standardized containers, governance, and self-service workflows for deep learning and generative AI training. Partner with platform and data engineering teams on security, access, and infrastructure standards. Lead responsible adoption of AI-assisted development tools and coach engineers on secure, compliant validation practices.

Architect and build reliable, scalable AI platform services supporting model training and inference across hybrid-cloud environments. Develop reusable APIs, SDKs, tooling, and reference architectures; establish observability, resilience, security, automation, and cost-optimization standards. Partner with engineering and product teams to deliver platform capabilities, meet reliability targets, resolve production incidents, and drive remediation. Mentor engineers and promote responsible, secure use of AI-assisted development tools.

Lead research and development of Transformer-based and time-series foundation models for systematic trading. Responsibilities include pre-training models from scratch on large-scale financial datasets, designing representations and self-supervised objectives, developing distributed training systems, fine-tuning models for trading applications, studying scaling and regime robustness, and evaluating economic performance through simulations and live-trading metrics.

Design and deliver enterprise-grade machine learning systems and AI-powered applications for banking. Build scalable, secure, distributed applications and automated cloud, desktop, and ML pipelines. Apply MLOps practices for versioning, reproducibility, and observability while collaborating with cloud and site reliability engineering teams. Develop reusable libraries and business-critical, data-intensive solutions, aligning machine learning problems with business objectives. The role may include mentoring or people leadership.

Designs, develops, and troubleshoots secure, scalable ML systems and services. Builds production software in Python using ML frameworks, cloud-native technologies, distributed systems, microservices, and NoSQL databases. Creates architecture artifacts, analyzes large datasets, improves system reliability and performance, and uses AI-assisted development tools responsibly. Preferred experience includes GPU workloads in Kubernetes, model-serving frameworks, distributed inference, model compression, and edge deployment.

Lead a data science team delivering analytics, machine learning, and AI solutions for wealth management. Partner with product, strategy, and operations stakeholders to frame problems, define success metrics, analyze client and portfolio outcomes, operationalize experimentation, and communicate executive-ready recommendations. Coach data scientists, develop scalable analytical capabilities, and identify high-value AI and agentic use cases that improve decision quality, client outcomes, and operational efficiency.

Design, build, productionize, and operate supervised and unsupervised ML models, LLM solutions, risk features, and Databricks data pipelines for financial crime monitoring. Develop AI feedback and accuracy measurement, monitor model drift, bias, and performance, and support enterprise-grade ML applications in production. Collaborate with product, engineering, SRE, data science, compliance, and operations teams using agile processes.

Embeds with Markets, Banking, and Payments teams to identify high-value generative AI opportunities. Translates business needs into technical requirements, reusable components, and production-grade solutions. Owns architecture and development across data, retrieval, prompting, orchestration, evaluation, deployment, and iteration. Partners with Technology and data science teams to meet enterprise resilience, controls, and risk standards while delivering measurable commercial impact.

Lead a team of ML engineers building and operating scalable risk features, data pipelines, supervised and unsupervised models, LLM solutions, and explainability systems for financial crime monitoring. Responsibilities include productionizing ML applications, monitoring drift and bias, optimizing performance, developing Databricks pipelines, and collaborating with product, SRE, compliance, and operational teams.