AI Research Scientist

• Engage in relevant research and development (R&D) of AI systems, models, and advanced machine learning algorithms that augment physics-driven modeling and simulation systems
• Explore and implement AI-powered simulation tooling in support of AI workflows through reinforcement learning, multi-agent systems, and hybrid modeling approaches
• Collaborate with research, engineering, and product teams to build AI-powered solutions that meet mission-critical modeling and decision-support needs
• Engage in and support the drafting and review of conference and journal articles and presentations, sharing advances with both internal stakeholders and the wider research community.
• Contribute content to technical invention disclosures, including associated narrative, graphics, and engagements in support of patent development
• Perform additional responsibilities (no more than 10% of duties) in support of the company’s technology and product development initiatives.

AI Research Scientist, Applied AI

AI Engineer

• Partner with a cross-functional team of engineers, research scientists, technical program managers, and product managers to deliver AI-powered products.
• Design, develop, test, deploy, and support AI software components including foundation model training and large language model inference.
• Leverage a broad stack of Open Source and SaaS AI technologies such as AWS Ultraclusters, Huggingface, and PyTorch.
• Invent and introduce state-of-the-art LLM optimization techniques to improve the performance of large scale production AI systems.
• Contribute to the technical vision and the long-term roadmap of foundational AI systems at Capital One.

Senior Lead AI Engineer, Gen AI Platform

• Partner with a cross-functional team of engineers, research scientists, technical program managers, and product managers to deliver AI-powered products that change how our associates work and how our customers interact with Capital One.
• Oversee the design, development, testing, deployment, and support AI software components including foundation model training, large language model inference, similarity search, guardrails, model evaluation, experimentation, governance, and observability, etc.
• Make high judgment build-vs-buy decisions across a broad stack of Open Source and SaaS AI technologies such as AWS Ultraclusters, Huggingface, VectorDBs, Nemo Guardrails, PyTorch, and more.
• Invent and introduce state-of-the-art LLM optimization techniques to improve the performance — scalability, cost, latency, throughput — of large scale production AI systems.
• Contribute to the technical vision and the long term roadmap of foundational AI systems at Capital One.
• Attract and retain top talent in the AI industry and nurture personal and professional development for your team.
• Foster a culture of learning and staying abreast of the state-of-the-art in AI.

Senior Manager, AI Engineering – Gen AI Platform Services

Machine Learning Engineer

• Lead end-to-end machine learning initiatives from ideation and prototyping through experimentation, deployment, and large-scale productionization.
• Design, develop, and deploy machine learning systems that operate across hundreds of millions of content signals using both real-time and batch processing architectures.
• Advance Spotify’s capabilities in natural language understanding, multimodal AI, and content intelligence. Build and evaluate LLM-powered solutions using modern prompting techniques, retrieval systems, and advanced model orchestration approaches.
• Define rigorous evaluation methodologies including golden datasets, precision and recall frameworks, offline benchmarking, and online experimentation.
• Partner closely with Product Managers, Engineering Managers, Staff Engineers, and Data Scientists to influence technical strategy and roadmap decisions.
• Mentor engineers across the organization and help elevate machine learning engineering standards and best practices.
• Contribute to the adoption of AI-assisted development workflows and tooling that improve team productivity and engineering effectiveness.

Senior Machine Learning Engineer – Content Intelligence

• Prototype, optimize, and productionize large-scale ML models that help deliver key results
• Conduct A/B experiments to validate the efficiency of ML models and pipelines
• Collaborate closely with product managers, ML engineers, full-stack engineers, and designers on a product team to protect ~100 million users
• Push the state of the art and apply the latest advances in deep learning and other machine learning techniques to improve trust and safety on Etsy
• Share impactful and innovative work in the wider ML research community, including presenting at top-tier ML/DS conferences such as: KDD, WSDM, WWW, Recsys, etc.

Software Engineer II, Machine Learning, Risk Engineering

• Own and improve applied AI and ML systems that influence important business workflows.
• Design, train, and deploy models that generate optimal, fair, and explainable offers for claims, balancing business objectives with fairness constraints.
• Build and refine production workflows around generation, decisioning, retrieval, evaluation, and orchestration.
• Work on concrete systems such as position statement generation, offer engine improvements, open negotiation agents, and configurable rules or decisioning systems.
• Design and run experiments that improve model quality, workflow quality, and business outcomes.
• Partner with product, operations, and engineering teammates to define success metrics and translate messy real-world problems into buildable systems.
• Improve prompt, model, and workflow behavior through tight feedback loops and practical iteration.
• Contribute to the engineering quality of these systems, including instrumentation, testing, rollout safety, and operational visibility.
• Use AI as a force multiplier in your own work and help the team move faster by bringing strong AI-native habits.
• Balance speed and rigor in an environment where shipping matters and iteration is constant.

Senior Applied AI Engineer – Offer Engine

• Develop innovative machine learning technology and products that serve millions of Snapchatters
• Work on state of the art GenAI pipelines for image, video, and audio generation
• Deliver generative machine learning experiences on device
• Build cutting-edge augmented reality experiences using generative and diffusion models
• Partner with cross-functional Snap teams to explore and prototype new products

Machine Learning Engineer, Generative ML

• Design AI solutions for customer experience use cases, including self-service, agent assistance, journey management, and back-office automation.
• Support solution designs that combine LLMs, deterministic workflows, tools, orchestration layers, fallback strategies, and human handoff patterns.
• Support retrieval-augmented generation, knowledge, and content strategies that help ground AI responses in trusted enterprise data.
• Assist with approaches for content structure, chunking, retrieval, relevance, and freshness while balancing performance, latency, and governance considerations.
• Help define AI evaluation approaches and quality measurement strategies for customer-facing AI experiences.
• Support the creation of test sets, feedback loops, and success metrics tied to accuracy, containment, customer satisfaction, business impact, and adoption.
• Assist in designing contextual AI experiences that use real-time data, conversation state, customer information, interaction history, and external signals to create more relevant and personalized customer and employee experiences.
• Support solution design decisions related to scalability, latency, reliability, and cost efficiency in enterprise environments.
• Help evaluate trade-offs across model selection, caching, orchestration, architecture patterns, and production-readiness considerations.
• Leverage strong working knowledge of Genesys AI capabilities to articulate and demonstrate product value to customers and prospects.
• Support presales activities including technical discovery, solution design, product demonstrations, sandbox and trial engagements, AI integration guidance, and value assessments.
• Design and deploy AI prototypes in sandbox and/or customer development environments to validate use cases, integrations, latency, success criteria, and differentiated Genesys AI value.
• Partner with account teams and Professional Services to help transition successful prototypes into production pilots, including technical handoff, solution hardening, KPI alignment, and business outcome validation.
• Build and support integrations to third-party systems via RESTful APIs and emerging interoperability patterns, showcasing what is possible with Genesys Cloud AI solutions.
• Develop reusable technical assets and help enable Solution Consultants, partners, and account teams through workshops, coaching, and scalable technical content.
• Provide field feedback and customer insights to Product Management and Engineering related to AI product design, implementation considerations, and customer-driven enhancements.
• Engage with technical and business stakeholders to position Genesys AI as part of a customer’s broader customer experience, IT, and transformation strategy.

AI Architect

• Partner with a cross-functional team of engineers, research scientists, technical program managers, and product managers to deliver AI-powered products that change how our associates work and how our customers interact with Capital One.
• Design, develop, test, deploy, and support AI software components including foundation model training, large language model inference, similarity search, guardrails, model evaluation, experimentation, governance, and observability, etc.
• Leverage a broad stack of Open Source and SaaS AI technologies such as AWS Ultraclusters, Huggingface, VectorDBs, Nemo Guardrails, PyTorch, and more.
• Invent and introduce state-of-the-art LLM optimization techniques to improve the performance — scalability, cost, latency, throughput — of large scale production AI systems.
• Contribute to the technical vision and the long term roadmap of foundational AI systems at Capital One.

Lead AI Engineer – MLX, Agentic AI, Gen AI platform Services

Lead AI Engineer – Vision Model Customization, VLM

• Design, build, and/or deliver ML models and components that solve real-world business problems, while working in collaboration with the Product and Data Science teams
• Inform ML infrastructure decisions using understanding of ML modeling techniques and issues
• Solve complex problems by writing and testing application code, developing and validating ML models, and automating tests and deployment
• Collaborate as part of a cross-functional Agile team to create and enhance software that enables state-of-the-art big data and ML applications
• Retrain, maintain, and monitor models in production
• Leverage or build cloud-based architectures, technologies, and/or platforms to deliver optimized ML models at scale
• Construct optimized data pipelines to feed ML models
• Leverage continuous integration and continuous deployment best practices, including test automation and monitoring, to ensure successful deployment of ML models and application code
• Ensure all code is well-managed to reduce vulnerabilities, models are well-governed from a risk perspective, and the ML follows best practices in Responsible and Explainable AI

Senior Machine Learning Engineer, AI Foundations

Senior Lead AI Engineer – AI Foundations, LLM Core, Agentic AI

Lead AI Engineer – AI Foundations, LLM Core, Agentic AI

• Full-Stack Product Development
• Architecture & Maintenance: Architect, develop, and maintain end-to-end applications powering AI-driven financial products.
• Backend & APIs: Build scalable backend services and APIs that support intelligent workflows and automated decision-making.
• UI Development: Create intuitive, high-performance user interfaces that surface complex insights and enable interactive experiences.
• Real-Time Systems: Design systems that support real-time communication between users, data sources, and AI components.
• AI & Data Integration: Partner with research and machine learning teams to integrate AI capabilities into production environments.
• Data Pipelines: Implement and maintain pipelines that ingest, process, and manage structured and unstructured financial data.
• Operationalization: Support the deployment and operationalization of AI-powered features and workflows.
• Engineering Excellence: Establish testing, observability, monitoring, and reliability standards across applications and services.
• Optimization: Optimize system performance, scalability, and maintainability.
• Technology Evaluation: Evaluate and adopt emerging technologies across AI, software engineering, and financial infrastructure.
• Continuous Learning: Stay informed on developments in large language models, agent frameworks, financial technologies, and modern web architectures.
• Best Practices: Contribute to technical discussions and help shape engineering best practices across the organization.

Full Stack AI Engineer

• Lead the end-to-end development and deployment of advanced AI/ML solutions
• Design and implement production-grade machine learning systems
• Drive innovation in emerging AI capabilities
• Develop scalable data science and AI pipelines
• Mentor and guide junior scientists

Applied Machine Learning Scientist II – AI/ML, Fraud/Risk, GenAI, Agentic AI

• Innovate in a Startup-Like Environment; developing AI-powered systems for accounting workflows
• Collaborate Across Functions; working with AI/ML engineers, frontend developers, product managers, and designers
• Help to build core AI interfaces; such as Conversations, Workspaces, Grid, and Knowledge Base
• Ramp up with a mentor; transitioning into contributing independently with a focus on quality, testing, and user experience

AI Engineer, New Grad

• Define and execute the roadmap for runtime intelligence capabilities
• Partner with executive stakeholders to align runtime intelligence investments with enterprise priorities
• Lead development of core runtime capabilities: Context orchestration and state management, Memory systems, Retrieval systems, Personalization frameworks
• Establish observability standards across runtime intelligence systems
• Oversee end-to-end delivery of runtime intelligence platforms

Director, Machine Learning Engineering

• Design and implement scalable AI architectures, including: LLM-powered applications, Retrieval-Augmented Generation (RAG) systems, agentic / multi-step workflows, vector search and retrieval services, model serving and inference layers.
• Establish reusable platform services, APIs, and design patterns to accelerate delivery across multiple teams.
• Build and operationalize AI delivery pipelines: CI/CD for models, prompts, and workflows, prompt versioning and lifecycle management, evaluation and testing frameworks, model and artifact registries.
• Implement monitoring for response quality and hallucination control, latency, throughput, and system reliability, cost observability and optimization.
• Design AI systems with strong controls for data security and privacy auditability and traceability, entitlements and access controls, data lineage and governance.
• Lead delivery of production-grade AI systems with a focus on scalability and reliability, latency and performance optimization, operational readiness and support.
• Partner closely with data engineering and platform teams to integrate AI capabilities with Snowflake and Databricks environments, structured and unstructured data pipelines, APIs and enterprise data services, semantic and knowledge-layer architectures.

Director, AI Engineering – Agentic Platform

LLM Engineer

• Design end-to-end AI solutions on Dataiku's platform, leveraging Dataiku Agent Hub, Prompt Studio, LLM Mesh, and Knowledge Banks (Vector Stores), or Python-based frameworks where needed.
• Build and orchestrate multi-agent systems using Dataiku's Visual Agents (simple and structured), as well as code-based frameworks (LangGraph, CrewAI, Claude Agent SDK, OpenAI Agents SDK) as appropriate.
• Integrate and optimize LLM APIs across providers (OpenAI, Anthropic, Google Gemini, AWS Bedrock, Azure, open-source models via Dataiku's LLM Mesh), applying model routing strategies to balance cost, latency, and quality.
• Implement Retrieval-Augmented Generation (RAG) pipelines, including agentic RAG and GraphRAG, using Dataiku's Knowledge Banks with reranking, dynamic filtering, and document extraction capabilities.
• Work exclusively with the Marketing organisation, partnering across functions such as Demand Generation, Content Marketing, Product Marketing, Field Marketing, Marketing Operations, Brand, and Communications.
• Engage marketing stakeholders to gather business requirements, then go further: identify the underlying user or team pain points those requirements represent, and design solutions that address both the stated need and the deeper problem.
• Own projects end-to-end, from requirements intake and solution design through to build, deployment, and handover.
• Develop autonomous and semi-autonomous AI agents, using Dataiku's Agent Builder, custom Python-based architectures (LangGraph, CrewAI, Claude Agent SDK, etc.), or a combination of both. Exercise judgment on when to leverage platform capabilities and when to build custom solutions.
• Design and build Agent Tools beyond documented examples, including custom API integrations, data retrieval modules, decisioning logic, and automated workflows, pushing past out-of-the-box patterns to deliver solutions tailored to specific business problems.
• Build, publish, and consume MCP (Model Context Protocol) servers to enable agent-to-tool integration across systems, including designing custom MCP servers where needed.
• Develop evaluation and monitoring approaches for agent systems, combining Dataiku's built-in capabilities with custom instrumentation to measure reliability, accuracy, cost, and business impact in production.
• Design and maintain evaluation frameworks (evals) for LLM-based systems, measuring accuracy, latency, cost, and reliability in production.
• Adhere to data governance, security, and regulatory compliance requirements (EU AI Act awareness, responsible AI practices) for all AI solutions.
• Leverage Dataiku's Cost Guard and Quality Guard features to manage LLM spend, enforce usage policies, and maintain output quality standards.
• Work closely with analytics and data engineering teams to maintain metadata on reference datasets for LLM consumption.
• Create front-end user interfaces for AI applications using HTML, CSS, and JavaScript, within Dataiku's webapps framework, Dataiku Answers for chat-based interfaces, or standalone applications built with Vue.js and Node.js.
• Collaborate on UX design, ensuring internal stakeholders find AI solutions intuitive and responsive.
• Provide product feedback to the development team to improve the platform.
• Stay current with the rapidly evolving AI engineering landscape, agent frameworks, model capabilities, evaluation practices, governance requirements, and tools like MCP and A2A protocols.

Generative AI Engineer

• Contribute to the technical development of cutting-edge agentic frameworks designed to automate the discovery of novel prognostic and predictive models in oncology
• Responsible for building and refining "deep agents" capable of hypothesis generation, experimental design, and multimodal ML modeling utilizing foundation models
• Key technical contributor, working closely with senior scientists and engineers to implement system designs and ensure code quality
• Apply advanced scientific methodologies to develop new predictive models and utilize causal inference frameworks to analyze vast multimodal oncology data
• Collaborate with Research, Engineering & Data Science teams across Tempus’ expansive data science community to develop and deliver innovative computational solutions
• Work with leading pharmaceutical companies to identify where the Tempus platform can add value
• Skillfully navigate client interactions to extract and communicate impactful insights driving new R&D opportunities

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