Top Artificial Intelligence Jobs

Design, prototype, and productionize generative AI features and agentic workflows. Build Python services, TypeScript/React UIs, data pipelines, evaluation and instrumentation, and safeguards. Partner with product and stakeholders to measure impact, ensure reliability, and mentor engineers.

Design and build automation tools, dashboards, and systems to deploy and manage cloud-based infrastructure. Collaborate with cross-functional teams to analyze requirements, implement reliable full-stack solutions (React front-end, Go back-end), improve CI/CD pipelines, and maintain production stability while meeting government background/clearance requirements.

Lead research and engineering of production-grade LLM/GenAI systems for cybersecurity: design models and architectures, drive data labeling and evaluation, collaborate with product and engineering to deploy scalable solutions, mentor data scientists, set technical strategy, and advance AI safety, red-teaming, and applied ML innovations for detection and operational use.

Build and scale test automation and contract testing frameworks for AI-driven detection systems across cloud, agent, and browser extension surfaces. Expand unit, integration, contract, and end-to-end coverage, define AI efficacy testing, implement observability and quality telemetry, and collaborate with SRE and engineering to ensure reliable, high-performance SaaS deployments.

Design and build highly scalable cloud-native microservices and platform components for AI Detection and Response. Own architecture, testing, monitoring, and operational excellence; integrate with CrowdStrike services; troubleshoot distributed systems; mentor engineers and drive secure, reliable deployments at scale.

Own end-to-end quality for AIDR services across backend and UI: build reusable test frameworks, validate functional/performance/contract/chaos scenarios, increase automated coverage, mentor engineers, and improve observability and resilience for large-scale cloud systems.

Leads the strategy, design, development, and scaling of AI and digital products across Mercer. Owns the full product lifecycle, defines roadmaps and use cases, coordinates cross-functional delivery, integrates structured and unstructured data, and ensures data quality, governance, privacy, security, ethical AI, and regulatory compliance. Partners with senior business and product leaders to drive adoption, monitor KPIs, and deliver measurable business outcomes.

Lead design, build, and operation of AI-powered Growth experiences (conversational onboarding and AI-driven email). Own technical strategy for an AI Voice Agent and ACE-powered email systems, design APIs and frontend/backends, partner cross-functionally, establish applied-AI engineering patterns, measure impact, and mentor engineers to improve product quality and responsible AI use.

Drive company-wide AI adoption by embedding with cross-functional teams to identify use cases and build production AI workflows, agents, internal tools, prompt/skill libraries, evaluation frameworks, and safe deployment patterns. Provide training, office hours, and enablement while partnering with security, IT, legal, and clinical stakeholders to ensure PHI-safe guardrails and measure adoption, productivity, and quality improvements.

Convert ambiguous healthcare and operational problems into well-scoped ML/AI, ranking, optimization, NLP, or LLM tasks; build and evaluate models, run error analysis, design honest evaluations, partner with engineering and clinical stakeholders, and productionize reliable models with clear communication of tradeoffs and limitations.

Lead architecture, design, and delivery of large-scale GenAI-enabled, distributed systems. Manage and mentor engineering teams, define standards and APIs, integrate LLMs and agentic workflows, ensure production readiness, observability, scalability, and partner cross-functionally with Product, AI/ML, Data Engineering, and DevOps.

Lead architecture, design, and delivery of GenAI-enabled, large-scale distributed systems across frontend, APIs, services, data, and AI layers. Define standards, guide technical reviews, ensure production readiness, partner with cross-functional teams, and mentor engineers to drive scalable, reliable, and maintainable platform solutions.

As a Staff Machine Learning Engineer, you will design AI products on Edge devices, optimize ML model performance, and work with large-scale datasets, collaborating across teams to deliver impactful solutions.

Own and grow Dynatrace's AI-native partner ecosystem for app builders and AI SaaS. Build executive relationships, execute co-sell motions, drive partner-influenced pipeline and revenue, run joint GTM campaigns, support events and enablement, and collaborate with Product on AI observability integrations and roadmap.

Lead AI-focused marketing strategy and ecosystem to position MongoDB as the default data platform for agentic applications. Own AI narrative, visibility in AI systems, ecosystem integrations and partnerships, documentation and training-data accuracy standards, and acquisition channels for agentic builders. Drive cross-functional alignment and measurable outcomes with product, engineering, and partner teams.

Configure, integrate, and deploy the BT tool stack (Snowflake, Salesforce, Streamlit, workflow tools). Build integrations and Streamlit apps, deploy AI-enabled automations, maintain Snowflake data models and reports, catalog assets with DataHub, implement SDLC/testing/guardrails, ensure data security, and partner with Engineering/InfoSec while mentoring BT team members.

As Product Manager II for AI & Data Security, you'll own the roadmap for AI capabilities, manage security posture, collaborate with engineers and researchers, and engage customers to enhance AI systems security.

The engineer will build and operate AI/ML infrastructure, managing services on AWS and bare metal, using tools like Kubernetes and Terraform.

Build and deliver AI-native, agentic systems for healthcare customers end-to-end: discover customer needs, architect and code solutions, manage onboarding and production reliability, mentor newer builders, and ensure compliance with federal and healthcare data protections.

Build verification and evaluation systems that allow AI agents to determine when work is complete. Develop testing environments, convert failures and human feedback into evaluation signals, improve agent context and memory delivery, and operate the agent fleet as a production system. The role spans application code, infrastructure, testing, measurement, and ambiguous problem-solving. Prior voice, model training, or product-domain experience is not required.

Lead deployment, inference optimization, resource scheduling, profiling, and stability initiatives for AI models across autonomous vehicles and cloud infrastructure. Design low-latency inference pipelines, optimize GPU execution and memory usage, support LLM serving and model validation, diagnose software-hardware integration issues, and collaborate with perception, prediction, cloud infrastructure, and safety teams.

Own and scale Assort Health’s business value function by developing executive-grade ROI models, business cases, value methodologies, calculators, and realized-value analyses for health systems and provider groups. Present analyses to executives, support strategic deals and renewals, train go-to-market teams, and partner across Sales, Customer Success, Product, Marketing, and Finance. Build the value playbook from scratch and eventually hire and lead the function.

Develop computer vision and vision-language models for image and video understanding in restaurant environments. Build datasets, training strategies, evaluations, and benchmarks; diagnose model failures; and deploy efficient, reliable inference pipelines. The role combines research and engineering across scene understanding, object tracking, activity recognition, temporal reasoning, visual grounding, and multimodal models, while partnering with product and infrastructure teams to bring research into production.

Leads Snorkel AI’s GTM systems strategy, Salesforce architecture, data governance, automation, and AI transformation. Manages a technical systems team, owns the GTM technology roadmap and vendor relationships, improves lead-to-cash processes, and partners across Sales, Delivery, Marketing, Finance, Legal, and Customer Success. The role drives scalable workflows, reliable revenue data, AI-enabled pipeline and forecasting solutions, and technology adoption across the organization.

Design AI systems that automate fundamental equity research, analyze financial data to generate earnings predictions and price targets, and integrate fundamental insights into systematic trading strategies. The role researches industry sectors, optimizes AI workflows, and supports iterative testing of fundamentally driven strategies. Candidates need finance or related education, at least two years of fundamental equity analyst experience, strong financial modeling skills, and the ability to translate investment insights into systematic strategy inputs.