Top AI & Machine Learning Jobs

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.

Build and deploy production-grade GenAI systems for strategic enterprise customers. Responsibilities include discovering AI use cases, designing RAG pipelines and agentic workflows, developing backend and frontend integrations, evaluating and securing AI systems, hardening prototypes into monitored services, and creating reusable tooling and patterns. The role owns customer-facing technical delivery, collaborates with business and engineering stakeholders, and requires travel up to 30%.

Owns the technical direction for AI agents and conversational HR capabilities. Responsibilities include agent architecture, autonomy boundaries, prompts and tool contracts, evaluation infrastructure, safety and observability, access-controlled retrieval, production quality, migrations, and AI-assisted engineering standards. The role partners across product, engineering, and design while remaining hands-on with complex integrations, prototypes, incidents, and systemic fixes.

Leads development of ServiceNow’s agent execution harness, including orchestration, multi-agent coordination, memory, observability, prompt infrastructure, evaluation frameworks, LLM integrations, reliability, and cost optimization. The role requires shipping production generative AI systems, designing scalable distributed software, establishing agentic architecture standards, and mentoring engineers.

Leads client-facing engineering engagements that design, build, evaluate, govern, and deploy production-grade AI agents on Anthropic’s Claude stack. Responsibilities include solution architecture, MCP server and Agent Skill development, prompt and context engineering, model strategy, observability, cost controls, security, and evaluation frameworks. The role mentors distributed engineering teams, guides enterprise adoption, partners with client stakeholders, and creates reusable agentic delivery patterns.

Leads the strategy, roadmap, architecture, and production scaling of agentic AI capabilities across a Databricks-based data platform on AWS. The role oversees AI workflows for data modeling, pipeline creation, governance, classification, quality, and self-service access, with strong emphasis on security, observability, auditability, responsible AI, and human validation. It also involves hiring and developing engineering teams, governing SDLC automation, and communicating technical investments and risks to senior leadership.

Leads the design, development, and deployment of secure, resilient cloud-native applications and agentic AI systems. Builds microservices on AWS using Terraform and services including ECS, EKS, Lambda, RDS, S3, and API Gateway. Develops multi-agent workflows with modern AI frameworks, improves operational stability through automation, and promotes responsible AI-assisted engineering practices. Provides technical leadership, mentoring, troubleshooting, and adoption of scalable architecture, CI/CD, security, and resiliency best practices.

Leads software engineering for AI/ML products within an agile banking technology team. Develops, reviews, debugs, and operationalizes secure production code using Python, PySpark, Databricks, AWS, and LLM technologies. Establishes governance and validation standards for AI-assisted engineering, promotes responsible AI practices, guides technical design and operations, and serves as a subject matter expert across teams.

Drive customer adoption of BrazeAI by leading workshops, diagnostics, and accelerators from AI use-case ideation through launch and impact measurement. Advise technical and marketing stakeholders, identify adoption barriers, develop AI playbooks and prototypes, recommend services partners, and provide AI thought leadership. The role combines customer-facing consulting, marketing technology expertise, applied AI knowledge, and cross-functional collaboration, with travel up to 25% at peak times.

Lead customer engagements that accelerate adoption of BrazeAI and agentic AI capabilities. Run workshops, diagnostics, and accelerators; guide customers from use-case ideation through prototyping, launch, and impact measurement. Build trusted relationships with technical and marketing stakeholders, resolve adoption barriers, recommend services partners, create best-practice materials, and provide AI marketing thought leadership. The role requires strong Braze, applied AI, mar-tech, facilitation, consulting, and ROI measurement expertise.

Drives customer adoption of BrazeAI by leading workshops, diagnostics, and 2–6 week accelerators. Identifies and prioritizes AI use cases, guides customers from ideation through agent launches, resolves technical and organizational barriers, documents best practices, and advises on ROI. The role builds trusted relationships with technical and marketing stakeholders, provides AI thought leadership, collaborates cross-functionally, and supports the AMER region from New York with travel up to 25%.

Develop and evaluate advanced machine learning methods for biomedical, clinical, molecular, and scientific data. Build foundation models, multimodal and graph-based systems, generative methods, and agentic reasoning tools. Design rigorous experiments, benchmarks, and validation frameworks; assess robustness, uncertainty, interpretability, and scientific utility. Collaborate with researchers, engineers, computational biologists, and domain experts to translate research into reusable capabilities supporting drug discovery and development.

Build and operate SoFi’s internal AI platform through production-grade microservices, telemetry pipelines, AI integrations, and developer tooling. Own feature delivery, architecture, testing, deployment, incident response, and operational excellence. Collaborate with partner teams and internal customers, improve platform adoption, participate in design reviews, and mentor engineers. Apply AI-assisted development responsibly while protecting sensitive data and maintaining rigorous security, testing, and governance standards.

Lead the Prism engineering team building SoFi’s internal AI productivity platform. Manage and develop software engineers, oversee AI infrastructure such as model harnesses, routing, and evaluations, define the technical roadmap, and partner with internal developer customers to drive platform adoption. The role requires people management, AI developer-tooling expertise, platform engineering experience, and the ability to deliver rapidly in an ambiguous, greenfield environment.