Top AI & Machine Learning Jobs

Develop and optimize video processing applications and algorithms for desktop and mobile. Research and evaluate video processing and neural-rendering methods, implement modular, high-performance code, integrate and deploy deep learning models across Mac, Windows, iOS and Android, and build test environments and tools for runtime verification.

Build and architect AI Agent capabilities for Zoom’s collaboration platform using Python and Java. Design distributed systems, APIs, microservices, databases, middleware, and cloud-native deployments supporting multimodal agents, real-time communication, meeting automation, and intelligent task management. Collaborate with product, design, and research teams to integrate LLM functionality, optimize performance, mentor global engineers, and coordinate delivery across international R&D centers.

Supports the design, development, testing, and deployment of UiPath RPA and agentic AI automation solutions. The intern integrates AI agents, bots, APIs, databases, applications, and enterprise systems; analyzes business processes; develops proofs of concept; documents workflows and technical specifications; and assists throughout the software development lifecycle. The role also involves responsible AI, security, governance, monitoring, troubleshooting, and collaboration with technical and non-technical stakeholders.

Designs and deploys AI and machine learning solutions for clinical and pharmaceutical challenges. Develops FastAPI backend systems, data pipelines, automations, and AWS ECS applications using Python, knowledge graphs, and large language models. Leads concurrent projects, integrates and analyzes complex datasets, collaborates with clinical, technology, business, and vendor teams, and communicates technical insights to stakeholders. Applies pharmaceutical and healthcare knowledge to deliver reliable, scalable, and reproducible solutions.

Leads multiple AI/ML engineering teams building an enterprise AI platform. Provides technical direction for LLM architectures, MLOps, model integration, validation, compliance, data governance, and high-performance ML systems. Owns the platform roadmap while managing resources, budgets, schedules, risks, cross-functional execution, and technical escalations. Mentors senior engineers and technical leads and supports integration events, customer reviews, and working groups. Travel is required up to 20%.

Leads multidisciplinary AI engineering and business advisory teams delivering AI solutions to business users. Owns use-case handoff through engineering delivery, schedules, budgets, compliance, risk mitigation, stakeholder collaboration, and executive reporting on adoption and ROI. Develops AI-driven solutions, builds relationships with business leaders, identifies opportunities, and mentors staff while ensuring adherence to security, data, and engineering standards.

Design, deploy, and optimize LLM platform services including gateways, model routing, workflow orchestration, integrations, and prompt evaluation frameworks. Build reliable AI and agent integrations with enterprise systems, test and debug services under production-like loads, monitor performance and costs, and maintain platform SLOs. Collaborate with AI, software, and architecture teams, document technical solutions, support compliance validation, and participate in on-call rotations.

Lead enterprise AI tooling strategy, governance, roadmap, and lifecycle. Maintain an enterprise inventory of AI tools, manage vendor relationships, drive tool evaluation/onboarding/retirement, ensure security and Responsible AI alignment, and enable cross-functional adoption and optimization across engineering, product, security, procurement, and governance teams.

Leads scalable simulation-data systems for robotic motion AI, including synthetic data generation, domain randomization, recovery scenarios, curriculum design, trajectory replay, and dataset reproducibility. Develops and evaluates machine-learning models for safety-critical robotic systems using physics-based simulators, diagnoses failures quantitatively, and validates models on physical robots. Collaborates with foundation model, policy, testing, regulatory, product, and clinical teams.

Leads the architecture, development, and production deployment of AI agents and cloud-native platforms on GCP. Builds solutions with LangChain, LangGraph, RAG, prompt and context engineering, MLOps, observability, and scalable microservices. Oversees engineering metrics, DevSecOps, FinOps, troubleshooting, technical strategy, documentation, agile delivery, and stakeholder presentations. Manages and mentors cross-functional software, quality, and reliability engineering teams while driving reliable, efficient, production-ready AI systems.

Hands-on product manager who designs, prototypes, and ships AI-powered GTM agents on Databricks. Build workflows and tools in notebooks and SQL, define agent behavior and governance, run experiments/A-B tests, own a multi-quarter roadmap, and iterate from structured seller feedback to improve sales processes end-to-end.

Design and build production agentic systems for creative video workflows. Responsibilities include developing agent architectures, integrating advanced models, enabling context gathering and tool use, and improving agent quality through evaluation, experimentation, and user feedback. The role spans product, machine learning, and systems engineering, with end-to-end ownership in a fast-paced startup environment.

Develop agentic systems powered by large language models for multimodal creative tasks, especially video analysis and editing. Responsibilities include creating training objectives, datasets, fine-tuning strategies, evaluation frameworks, and structured-generation methods; improving reasoning, tool use, control, and reliability; running systematic experiments; and analyzing failure modes across planning, execution, and deployment. The role requires strong ML engineering experience, knowledge of transformers and modern LLM techniques, and ownership across model experimentation and production agent systems.

Develop and deploy predictive and causal models for personalization, retention, monetization, and product strategy. Design experimentation and measurement frameworks, analyze user and marketing behavior, quantify business impact, and translate complex findings into strategic decisions. Collaborate with Product, Engineering, Marketing, and Data Product teams to build scalable data solutions. Mentor peers, promote rigorous model validation and governance, and apply Python, SQL, Snowflake, dbt, Airflow, Spark, and AWS to production-ready data science initiatives.