Top AI Engineer Jobs

Build and scale WHOOP AI platform systems and member-facing experiences across the frontend and backend. Design scalable web applications, APIs, and intuitive interfaces; contribute to agent orchestration architecture; translate business requirements into reliable solutions; write tested code; and monitor, debug, and improve production performance and stability. Collaborate with product managers, designers, data scientists, and other cross-functional partners in a fast-paced environment.

Build and maintain backend infrastructure and member-facing generative AI features that deliver personalized health and performance insights. Collaborate with Product, Design, Analytics, and Data Science teams to define requirements and deliver scalable solutions. Develop reliable software using Java, Kafka, AWS, and PostgreSQL; design RESTful APIs and relational databases; optimize performance; and maintain observability and monitoring systems.

Build and operate production AI systems powering WHOOP Coach, AI Support, and other intelligent product features. Lead AI/ML initiatives across data pipelines, dataset design, LLM fine-tuning, evaluation, deployment, experimentation, observability, and continuous feedback loops. Partner with product and data science to create reliable systems based on member needs and real-world usage. Mentor engineers and data scientists while establishing best practices for applied AI/ML, privacy, security, and production lifecycle management.

Build and maintain performant, responsive, mobile-first web applications using Next.js, React, and Tailwind CSS. Translate product goals into polished member-facing experiences, prototype and ship features with AI-assisted workflows, integrate RESTful APIs, and collaborate with product, design, and backend teams. Establish frontend best practices for architecture, accessibility, performance, and maintainability while contributing to UX decisions and navigating ambiguous problems.

Design, train, deploy, monitor, and optimize AI/ML models powering a cloud-based voice agent platform. Responsibilities include STT, NLU, and TTS development; MLOps pipelines; AI API services; inference and latency optimization; frontend prototypes and dashboards; research, experimentation, vendor evaluation, documentation, and code quality. The role requires collaboration with backend, frontend, and infrastructure teams.

Unpaid AI engineering internship to design, build, and deploy ML solutions (NLP, computer vision, recommenders). Work on data preprocessing, model training/fine-tuning, containerized FastAPI microservices, and scalable deployments on Google Cloud Run. Contribute to code reviews, GitHub repos, and integration of third-party AI APIs.

Lead the architecture, build, and deployment of AI-driven automation and intelligent systems to solve security, compliance, and risk challenges. Develop real-time intelligence, workflow automation, and risk-based decision-making tools, and engage customers and partners to refine and deploy production-grade solutions.

Build and own conversational voice AI products end-to-end: architect, implement, and deploy the platform. Design infrastructure, develop front-end and back-end systems, deliver reliable customer outcomes, and collaborate directly with the CTO to scale the product for enterprise customers.

Supports generative AI application development by evaluating model quality, safety, accuracy, and performance; debugging and optimizing applications; preparing and validating data; and designing secure, scalable solutions with AWS Bedrock and Anthropic Claude. Reviews Python and ReactJS code, documents technical artifacts, tracks tasks, conducts research, and participates in team meetings.

Design and build a production multi-agent LLM framework and LLM-powered pipelines (text-to-SQL, NLU, structured reasoning). Implement RAG, prompt systems, evaluation and CI/CD for AI features, instrument and optimize model performance, and collaborate with product to deliver user-facing capabilities.

The Data & AI Engineer III will lead cloud data engineering solutions, develop data infrastructure, establish architectures, mentor team members, and secure data around cloud systems.

As a Staff AI Engineer, you'll lead AI product development, overseeing technical direction, system deployment, and model evaluation while collaborating across teams.

The role involves designing AI systems for mental healthcare, focusing on building scalable, production-grade systems that enhance patient engagement and clinical operations. Responsibilities include setting technical standards and ensuring safety and reliability while working with advanced AI technologies.

Lead the development of AI software components for Capital One, including model training, inference, and system optimization. Collaborate with cross-functional teams to deliver groundbreaking AI solutions, ensuring quality and scalability while mentoring junior team members.

As a Lead AI Engineer at Capital One, you will design and develop AI systems, collaborate with cross-functional teams, and optimize AI solutions for performance and scalability.

Lead design, development, deployment, and optimization of large-scale AI/ML systems (training, inference, orchestration, observability). Collaborate cross-functionally to build responsible, scalable generative AI and agentic platforms, improve performance and cost, and shape long-term ML systems roadmap.

The role involves partnering with cross-functional teams to design, develop, and deploy AI software solutions, focusing on large language models and AI optimization techniques.

Build, integrate, deploy, and operationalize secure AI solutions across enterprise platforms. Responsibilities include prompt engineering, orchestration, RAG implementation, enterprise data integration, monitoring, performance tuning, documentation, and governance compliance. The role partners with IT, Security, Privacy, Data, and business teams to deliver production-ready AI capabilities using technologies such as OpenAI, Anthropic, Microsoft 365, Salesforce, and ServiceNow.

Own the generation layer of Document Intelligence by designing prompts, schemas, validators, multimodal context pipelines, and quality evaluations. Build production LLM features that transform video, audio, PDFs, images, OCR, transcripts, and keyframes into validated maintenance entities. Balance model quality, cost, and latency, monitor regressions, and collaborate with processing and domain engineering teams.

Leads hands-on development of production AI agents and workflows, including architecture, evaluation, monitoring, deployment, and performance optimization. Mentors engineers, sets technical standards, translates customer and stakeholder needs into technical plans, and drives tooling and process improvements. Collaborates across business functions to identify automation opportunities and contributes to the LangChain and LangGraph open-source ecosystems.

Leads data and AI engineering initiatives by translating business and customer needs into technical objectives and solutions. Designs large-scale data architectures, evaluates and builds AI and generative AI tools, develops capability roadmaps, and supports enterprise customers. Works hands-on with developers, mentors data engineers, establishes engineering best practices, conducts code reviews, and facilitates cross-functional delivery. Contributes to business development, strategic solutioning, data-driven insights, operational decisions, and performance management.

Leads the design, prototyping, development, deployment, and governance of generative and agentic AI solutions. Builds and fine-tunes machine learning models, manages data pipelines, develops scalable Python services, and applies MLOps practices in containerized environments. Researches emerging AI technologies, leads proof-of-concept initiatives, and collaborates with engineers, architects, product managers, and business stakeholders to deliver production-ready AI systems.

Designs, develops, deploys, evaluates, secures, and maintains enterprise AI, machine learning, generative AI, and LLM solutions for government applications. Responsibilities include data preparation, model training and monitoring, evaluation, red-team testing, safety controls, documentation, governance, privacy protection, cybersecurity compliance, architecture reviews, and stakeholder collaboration. The role translates mission and technical requirements into production-ready, responsible AI capabilities.

Build backend services and AI systems for legal drafting, review, and search workflows. Model legal knowledge into ontologies, develop retrieval and evaluation infrastructure, test LLM prompts, optimize queries and performance, and deploy solutions with customers. The role also includes occasional frontend work in TypeScript and React, customer collaboration, and end-to-end ownership of product features.

Lead AI/ML security architecture and engineering initiatives, clarify security requirements across multiple networks, assess and triage threats, manage disruptive-event response resources, and recommend risk-based business modifications. Build enterprise security solutions through hands-on coding, threat modeling, vulnerability assessment, penetration testing, cloud-native development, and security integration into CI/CD workflows.