Top AI & Machine Learning Jobs

Design, train, and evaluate deep neural networks, foundation models, vision-language models, and AI agents for telecom use cases. Reproduce research-paper methods, run scalable experiments on cloud platforms or GPU clusters, and collaborate with researchers and engineers to move proofs of concept into production-ready solutions for cellular networks.

Develops advanced statistical, predictive, machine learning, and LLM-based models to generate business insights and build data-driven products. Creates evaluation frameworks, monitors deployed AI systems for performance and drift, extracts data from diverse platforms, and develops complex data models. The role also requires mentoring peers, collaborating across teams, managing projects, and delivering measurable business impact.

Design, develop, and deploy AI applications using LLMs, enterprise APIs, agentic frameworks, and related technologies. Integrate AI models with existing products, build large-scale Java and Python applications, and use Kubernetes and Docker. Apply prompt engineering, fine-tuning, RAG, deep learning, NLP, and computer vision concepts. Troubleshoot complex application and model issues, stay current with AI advancements, and support advanced software development across cross-functional teams.

Build and deliver production-ready AI applications and agentic systems across React and TypeScript frontends, Python services, MCP infrastructure, RAG systems, and enterprise integrations. Responsibilities include designing MCP servers, tools, schemas, and agent workflows; implementing authentication and RBAC; integrating observability; supporting secure OpenShift or Kubernetes deployment; and contributing to testing, CI/CD, documentation, and production readiness.

Build and deliver production-ready AI applications and agentic systems across React and TypeScript frontends, Python APIs, MCP services, RAG systems, and agent orchestration frameworks. Design reusable tools, skills, schemas, integrations, and workflows; implement authentication, authorization, RBAC, observability, testing, and enterprise deployment practices. Collaborate with engineering, platform, product, and architecture teams while supporting secure, scalable deployments on OpenShift or Kubernetes.

Designs, develops, tests, deploys, and maintains AI agents and Generative AI applications using LLMs, LangGraph, LangChain, REST APIs, and MCP integrations. Monitors production systems, analyzes telemetry, troubleshoots issues, optimizes performance, and collaborates with data scientists, engineers, and business stakeholders to deliver scalable enterprise AI solutions.

Designs, develops, tests, deploys, and supports AI agents, generative AI applications, and agentic workflows. Builds REST API and Model Context Protocol integrations, automates data and analytics processes, and collaborates with data scientists and engineers. Monitors production AI systems using telemetry and observability tools, troubleshoots issues, optimizes performance, and contributes to scalable, governed AI engineering practices.

Develops, tests, deploys, and maintains AI-powered applications and agentic systems using Python, LLMs, LangGraph, LangChain, REST APIs, and MCP. Collaborates with data scientists, data engineers, and business stakeholders to build scalable enterprise AI solutions. Monitors production systems, analyzes telemetry, troubleshoots issues, and optimizes reliability, efficiency, and business impact. The role also supports AI automation, model operationalization, data workflows, governance, documentation, and software development best practices.

Develop and deploy AI-enabled full-stack applications for mission-driven customers. Responsibilities include building React and TypeScript front ends, Python or Node.js backend services, REST APIs, PostgreSQL data models, Docker containers, and OpenShift deployments. The role involves generative and agentic AI, prompt engineering, production troubleshooting, stakeholder collaboration, technical design, code reviews, automated testing, and documentation.

Leads data, analytics, and AI business initiatives from problem definition through solution design, development, testing, and deployment. Collaborates with business and technical stakeholders, delivers analytics solutions, applies statistical and machine learning techniques, guides digital transformation, develops reusable frameworks, and mentors analytics talent. The role requires leading mid-sized teams, deploying AI, working with diverse datasets, documenting technical solutions, and applying Agile delivery methods.

Designs, builds, evaluates, and deploys multi-step agentic AI systems, including autonomous and multi-agent architectures. Integrates LLMs with tools, APIs, databases, and code execution environments. Owns evaluation frameworks measuring reliability, safety, latency, cost, and task success. Collaborates with product, engineering, and research teams to deliver production-grade solutions while mitigating risks such as prompt injection, hallucination, and unsafe tool use.

Design, build, and evaluate multi-step agentic AI systems using autonomous agents, LLMs, tools, APIs, databases, and orchestration frameworks. Develop production-grade agent architectures, evaluation frameworks, and safety measures addressing prompt injection, hallucinations, reliability, latency, cost, and unsafe tool use. Collaborate with product, engineering, and research teams while applying current research to improve personalized travel experiences.

Design, build, evaluate, and deploy multi-step agentic AI systems, including autonomous and multi-agent workflows. Develop orchestration frameworks, integrate LLMs with tools and APIs, and create evaluation systems measuring reliability, latency, cost, safety, and task success. Collaborate with product, engineering, and research teams to deliver production-grade solutions while mitigating prompt injection, hallucinations, unsafe tool use, and other agentic-system risks.

Productionize machine learning for manufacturing while building and operating the data engineering foundations that support models. Responsibilities include designing ML workflows, ETL/ELT and feature pipelines, governed Bronze/Silver/Gold data products, model lifecycle management with MLflow and Unity Catalog, CI/CD, monitoring, legacy ETL modernization, operational support, and collaboration with data science, manufacturing, architecture, product, and platform teams.

Identifies and formalizes problems for algorithmic solution development and deployment using artificial intelligence, machine learning, data science, and related techniques. The role connects contextualized problems to data-driven research and practical solutions. This is a hybrid position requiring regular onsite attendance. General Motors does not provide immigration-related sponsorship for this role.

Designs, builds, and operates scalable data and AI engineering solutions across batch, streaming, and real-time environments. Responsibilities include developing data pipelines, AI data products, feature and embedding workflows, retrieval systems, MLOps/LLMOps practices, and cloud platforms. The role leads architecture decisions, production support, observability, security, governance, and performance improvements while partnering across technical and business teams. It also mentors engineers and drives delivery of trusted data products for machine learning, generative AI, analytics, and business intelligence.

Leads the strategy, architecture, engineering, and production delivery of AI, machine learning, and generative AI solutions. Directs cross-functional teams, establishes engineering and responsible AI standards, oversees LLM applications, RAG, agents, evaluation, deployment, monitoring, security, and lifecycle management. Manages talent, budgets, resources, stakeholder alignment, and continuous improvement while connecting AI investments to business value.

An 11-week paid internship focused on building AI agents and developer tools for software engineering workflows. The intern will create API integrations, workflow automations, evaluation datasets, regression tests, logging, and tracing using Python and/or TypeScript. Responsibilities include defining use cases, measuring performance against baselines, documenting design decisions, incorporating feedback, and presenting a working prototype. Collaboration, debugging, critical evaluation of AI outputs, and clear communication are essential.

An 11-week internship focused on designing, testing, refining, and documenting prompts for conversational AI, including chatbots and voice assistants. The intern will evaluate prompt performance, build prompt libraries, create AI-enabled workflows, collaborate with product and engineering teams, and present strategy and results to leadership. The role requires strong writing, analytical thinking, curiosity, and hands-on experience with LLMs.

Lead the architecture and implementation of production-grade agentic AI and simulation automation systems. Build AI agents integrating simulation engines, digital twins, APIs, datasets, NVIDIA platforms, cloud services, and GPU infrastructure. Establish evaluation, governance, observability, security, and human-approval frameworks. Provide technical leadership across teams, guide architecture and delivery, mentor engineers, and develop reusable platform components for simulation, robotics, autonomy, and engineering workflows.

Lead advanced digital and rental analytics using GA4, BigQuery, Python, and SQL. Analyze clickstream data, customer journeys, conversion funnels, attribution, and site performance to improve revenue and customer growth. Define KPIs, develop analytics roadmaps, apply statistical and machine learning methods, establish data governance, and translate insights into recommendations. Collaborate with senior leaders, product owners, strategy teams, and technical stakeholders while supporting scalable measurement solutions and data-driven product development.

Leads a multidisciplinary data science and AI pod focused on AI-assisted engineering tools, adoption, analytics, and productivity measurement. Develops technical plans, guides deployment and troubleshooting, supports Agile product ownership, reports actionable insights, and mentors data science, machine learning, GenAI, and software engineering professionals. Partners with users, product owners, architects, engineers, and business stakeholders to shape scalable enterprise AI enablement services while applying responsible AI principles.