Top AI & Machine Learning Jobs

Own the AI systems layer for a high-volume consumer product. Define the AI roadmap, partner with OpenAI on voice models and workflow automation, evaluate emerging technologies, and prototype new capabilities. Design evaluation frameworks, guardrails, and production architectures balancing latency, cost, accuracy, and reliability. Guide prompt engineering, build automations for document validation and disputes, and translate subject-matter expertise into dependable AI systems.

Own the architecture and roadmap for production AI systems supporting a high-volume consumer chatbot. Evaluate and deploy emerging technologies such as voice models and computer-use agents, partner with OpenAI, design evaluation frameworks and guardrails, and balance latency, cost, accuracy, and reliability. Build automations for document validation, record disputes, and communications while translating subject-matter expertise into dependable AI workflows.

Own the architecture and roadmap for production AI systems powering high-volume consumer chatbots. Evaluate and deploy emerging capabilities such as voice models and computer-use agents, build evaluation frameworks and guardrails, and balance latency, cost, accuracy, and reliability in a regulated environment. Develop automations for document validation and service workflows, partner with OpenAI and subject-matter experts, and establish technical standards for scalable LLM-based systems.

Develop and operate machine learning systems for automated manufacturing. Responsibilities include building scalable data and model infrastructure, automating ML pipelines, deploying models to production and edge devices, monitoring performance, optimizing latency, integrating AI/ML tools, managing incidents, ensuring security and privacy, and collaborating with automation engineers, data scientists, data engineers, and software teams.

Build and maintain AI/ML capabilities for Blue Ring’s satellite ground systems, including multi-agent telemetry analysis, anomaly detection, conversational retrieval, RAG pipelines, and LLM integrations. Develop and evaluate time-series models, support retraining workflows, and deploy containerized AI services on AWS. Contribute to observability, audit trails, incident response, human-in-the-loop operations, and production support while collaborating with senior engineers.

Lead forward-looking research on robot learning, hardware, supply chains, and commercialization. Produce reports, forecasts, and briefings; track mobile manipulators, quadrupeds, cobots, and autonomous vehicles; assess AI, edge compute, and simulation impacts; collaborate with cross-sector analysts; support investors, OEMs, and policy stakeholders; and present findings to senior executives and clients.

Conduct research on AI tokenomics by evaluating model architecture trends, building simulation models for training and inference efficiency across hardware and workloads, diligencing companies using model tokens, and forecasting economic impacts for investors, corporates, and policymakers.

Develop and deploy AI/ML tools for structural engineering: prepare and analyze datasets, apply Bayesian/MCMC inference, perform FEA-driven data preparation, use regression/clustering and constrained multi-objective optimization, and create visualizations to communicate results.

Lead AI research and innovation focused on LLMs, generative and agentic systems; prototype and productionize models; design experiments, evaluate results, and ensure explainability, security, and compliance; mentor teams and drive enterprise AI adoption across financial services.

Lead design, development, and operationalization of enterprise Generative AI solutions (LLMs, RAG, agentic AI). Architect scalable cloud deployments, implement GenAIOps, observability, and secure CI/CD pipelines. Mentor engineers and partner with product, risk, and security teams to deliver production-ready, compliant AI systems.

As a Machine Learning Engineer II at Capital Group, you will design and implement ML models and data pipelines to enhance investment decisions, collaborate with teams, and ensure quality in your work.

Conducts research using crewed and uncrewed tropical cyclone reconnaissance data to improve hurricane analysis and forecasting. Develops and evaluates machine learning methods for estimating tropical cyclone surface winds, creates real-time products for National Hurricane Center testing, runs and verifies HAFS forecasts, manages and visualizes complex data, uses high-performance computing, publishes findings, and presents results at conferences.

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.

Own end-to-end MLOps lifecycle: build and scale infrastructure, CI/CD, experiment tracking and model versioning, implement production monitoring and drift detection, manage cloud and GPU resources, and collaborate with data scientists to deploy production-ready ML models.

Own the end-to-end MLOps lifecycle: build and scale infrastructure, CI/CD, model deployment, monitoring, drift detection, experiment tracking, versioning, and manage cloud/GPU resources in collaboration with data scientists.

Own the technical design and delivery of an enterprise AI collaboration platform. Build production AI agents, workflows, RAG integrations, APIs, connectors, and secure integrations across Slack, Google Workspace, Glean, Claude, and ChatGPT. Administer platform access, configurations, monitoring, and lifecycle management; ensure security, governance, reliability, and data grounding. Lead architecture reviews, establish engineering standards, troubleshoot complex issues, mentor contributors, and evaluate emerging AI capabilities.

Analyze defense and integrated combat system training, test, and evaluation data; develop MATLAB or Python scripts for data extraction and transformation; prepare technical reports and presentations; communicate findings to government stakeholders; document workflows; and collaborate with multidisciplinary teams supporting government-led weapons system assessments.

This role focuses on developing AI/ML algorithms for various applications, requires teamwork with developers and researchers, and involves analyzing datasets and deploying ML models.

Develop and implement AI/ML algorithms across computer vision, NLP, time series, graph data, reinforcement learning, and large language models. Analyze multidomain datasets, apply current research, document technical solutions, and understand AWS-based training and deployment pipelines. Mentor team members and contribute within interdisciplinary research and development teams. The role requires Python and modern machine learning libraries, deep learning expertise, version control, and an active TS/SCI clearance with adjudicated polygraph.