Top Artificial Intelligence Jobs

Designs, implements, and deploys cloud-native AI/ML solutions and data pipelines to support personalization and optimization. Builds and serves ML models (real-time and batch), develops APIs and front-end tools, automates workflows, and ensures secure, scalable AWS infrastructure. Leads architecture discussions, mentors engineers, manages project lifecycle, and advises senior management on technical strategy and risk.

Own and scale recruiting operations: hire and lead recruiting coordinators, architect ATS and recruiting tech stack, build AI-enabled automations, create hiring dashboards and SLAs, enable hiring managers with guides and training, and partner on headcount planning to improve pipeline conversion and time-to-hire.

Build and scale agentic LLM systems (planning, tool use, memory, multi-agent coordination). Develop modular agent skills and services, own technical architecture, implement observability and automated evaluation, and collaborate with Product and Implementation teams to apply agents to customer problems.

Sell AI-driven solutions to mid-market businesses: prospect, qualify, consult with decision-makers, manage the full sales cycle, close deals, build long-term client relationships, and identify upsell opportunities with support from leadership and technical teams.

Responsible for recruiting top talent for Core Tech teams, focusing on Machine Learning and AI. Collaborates with hiring managers to improve processes and enhance employer branding while scaling diverse teams.

Build and optimize AI-first services and APIs for a developer sandbox using LLMs and custom ML models. Develop testable Python code, deploy and maintain ML services in production, implement observability, collaborate across teams, and mentor engineers to deliver scalable, developer-centric experiences.

Seeking Applied AI Research Scientists to develop and explore new AI approaches, algorithms, and methodologies to drive AI systems across S32 portfolio companies and shape company direction.

S32 seeks Applied AI Research Scientists to explore new approaches, algorithms, and methodologies to advance AI systems across portfolio companies. Candidates will drive research, shape company direction, and collaborate with founders to apply research outcomes to product and business needs.

Lead strategy, roadmap, and execution for Front's Copilot AI assistant embedded in the inbox. Define priorities, use quantitative and qualitative research, drive end-to-end product development, and partner with engineering, design, data, marketing, and sales. Deliver trusted, high-quality AI features (summarize, prioritize, draft, classify, route) and align stakeholders including executives.

Lead development of generative AI features and agents, scale platform to ingest and query terabytes of data in real time, optimize search and notifications, ship production-grade software across full stack, and collaborate with deployment teams and users to iterate on safe, impactful AI-driven user experiences.

Lead design, evaluation, and productionalization of AI features (LLMs, deep learning, RL). Build AI infrastructure, ensure model observability, monitoring, and reliability, and lead an applied AI team to integrate trusted, explainable capabilities into product workflows.

Lead development of generative-AI features and scalable backend systems. Design, ship, and iterate production-grade software that processes terabytes of real-time data, optimizes search/query performance, and integrates ML models to deliver safe, user-focused AI experiences.

The Manager, AI Engineering develops and deploys AI/ML and GenAI solutions, contributes to AI strategy, and ensures compliance with regulations.

Design and build AI- and automation-driven solutions to accelerate Mortgage Cadence Platform configuration using LLMs and workflow automation. Develop integrations with system APIs, partner with implementation teams, create documentation and training, and propose new tools to improve implementation accuracy and scale.

As an AI Scientist, you'll design and deploy AI solutions for intralogistics, collaborate with stakeholders, and create machine learning models and prototypes.

As the Generative AI Product Owner, you will define product vision, manage backlogs, build roadmaps, and ensure user needs are met while collaborating with stakeholders and development teams.

As an AI Infrastructure Engineer, you will manage user-facing services, ensure system reliability, and improve infrastructure using automation tools such as Ansible and Terraform while collaborating with various stakeholders.

The Principal Engineer will lead AI-driven productivity initiatives, develop a strategy for integrating AI in engineering workflows, and improve collaboration across teams. Responsibilities include executing projects, ensuring code quality, and setting success metrics.

Design and develop scalable infrastructure and services for data pipelines and distributed systems, working on an AI/ML platform for enterprise applications.

As a Senior Product Manager for Chess AI, you'll shape the AI/ML roadmap, partner with engineering, and define product success metrics, while collaborating with stakeholders to enhance user engagement and experience in chess.

The role involves developing and integrating ML/AI solutions in agricultural biotechnology, mentoring teams, and promoting data-driven practices.

The role involves designing, developing, and deploying AI systems, collaborating on workflows, integrating large language models, and ensuring high-quality code following best practices.

The Google AI Architect will design and deploy an AI bot on Google Cloud for billing processes and citizen services, ensuring compliance and scalability.

As a Senior Application Developer, you will define, develop, and deploy software solutions, collaborate with clients, and implement AI features within applications.

The Conversion Engineering-Global Content Consultant role focuses on consulting Global Data Conversion Software Engineering, improving processes, and implementing AI solutions. Responsibilities include supporting offshore teams, driving project completion, and creating documentation for standards.