Top AI & Machine Learning Jobs

Analyze high-volume subscription and app telemetry to drive marketing decisions: churn analysis, customer segmentation, retention patterns, media-mix and CLV modeling; apply advanced analytic techniques and present strategic recommendations to senior stakeholders.

Build end-to-end AI-driven automations across finance, operations, and clinical-support: browser automation for invoices, document data extraction, internal AI agents, data integrations, and fast prototypes. Work directly with the CAIO to turn loose specs into shipped tools.

Develop and train computer vision and visual representation pipelines for robotic manipulation (RGB, RGB-D, depth, segmentation, pose, keypoints). Build perception models to support RL and imitation policies, improve synthetic/real data pipelines, evaluate models on real robots, calibrate sensors, and collaborate with robotics and simulation engineers to iterate on models and data for robust deployment.

Design, build, and deploy custom AI chatbots, agents, and automated reports; integrate AI models with core systems via APIs; architect and maintain a unified knowledge base; ensure data privacy and access controls; convert AI outputs into presentations/spreadsheets and train executives to adopt automated workflows company-wide.

Develop and optimize inference runtimes for on-device and cloud deployments. Integrate inference engines, bring up new models and modalities, improve latency, throughput, memory, and reliability across CPU and GPU targets, build batching/scheduling/caching/distributed execution capabilities, benchmark and diagnose performance and correctness, and contribute upstream to open-source projects.

Join Capsule as an AI Engineer to develop ML models for video editing. Responsibilities include researching ML techniques, collaborating on feature development, and optimizing model performance.

As a Software Engineer at Variance, you'll build AI agents to combat fraud, develop features end-to-end, and collaborate with founders on large-scale issues in a fast-paced environment.

Lead and grow a hands-on engineering team building AI-native, member-impacting health products. Own end-to-end delivery from discovery through production, set technical direction, drive experiments and reliable deployments, and partner cross-functionally to improve clinical and business outcomes.

Design, build, and deploy AI-powered systems and intelligent agents. Integrate models into applications, develop scalable AI infrastructure, prototype and scale solutions with business stakeholders, and ensure ethical standards. Collaborate cross-functionally to streamline operations and enhance decision-making across the enterprise.

Embed with delivery teams to rapidly design, build, and demo LLM-powered agentic prototypes and internal tools. Translate ambiguous business problems into measurable prototypes, instrument evaluations and metrics, extract reusable components, and hand off clean artifacts to production engineering while maintaining subject-matter engagement and strong business judgment.