Top AI & Machine Learning Jobs

Lead enterprise AI product strategy and roadmap, prioritize high-value GenAI and agent-based use cases, translate business needs into product requirements, and partner with data, engineering, architecture, and governance to deliver scalable, production-ready AI solutions that drive measurable business outcomes and adoption.

The Director of Data Science leads the data architecture and team, transforming complex datasets into actionable insights and strategies that propel product development and business growth.

Lead design, build, and operation of production AI systems and inference stacks (vLLM) across AI Factory platforms. Implement RAG, MLOps, storage/networking, Kubernetes/containers, monitoring, security, and on-call incident response. Mentor engineers, engage clients, author runbooks, and contribute reusable patterns and tooling.

The role involves integrating and productionizing ML models into infrastructure, optimizing pipelines for real-time inference, and enhancing GPU workloads for robotics applications.

The Senior Machine Learning Engineer will manage the full ML lifecycle, collaborating with teams to develop scalable systems that improve product decisions and empower monetization strategies.

The Applied AI Scientist will develop AI features, improve orchestration and reasoning systems, and work closely with engineering teams on production capabilities.

As a Forward Deployed AI Engineer, you will integrate Latent Labs' AI models with pharmaceutical clients' workflows, focusing on tech deployment, customer advocacy, and collaborative feedback for product improvement.

The AI Engineer will design, develop, and deploy AI-powered systems, improve operations with intelligent workflows, and collaborate on high-leverage AI opportunities.

The AI Engineering Intern will develop and enhance Large Language Models to improve task workflows, automate processes, and prototype AI features for client productivity.

The Machine Learning Engineer will design, implement, and optimize reinforcement learning models for autonomous driving systems, enhancing data systems for scalable evaluation and performance assessment.