Top AI & Machine Learning Jobs

Build and improve AI recruiting agents using modern LLMs, Python backend services, APIs, prompts, RAG, workflows, and agent orchestration. Evaluate model performance, experiment with AI technologies, collaborate with product and engineering teams, and deploy scalable production systems. The role requires Python, LLM and AI API experience, familiarity with agent frameworks and FastAPI, and cloud platform knowledge. Production AI product experience, model evaluation, tool calling, prompt engineering, and open-source contributions are preferred.

Lead development of scalable molecular dynamics pipelines, integrating physics-based models with machine learning frameworks to enhance molecular engineering and experimentations.

Develops machine learning models and production data science pipelines to improve subscription conversion, content recommendations, user understanding, workflow automation, and product decisions. Leads analytical projects and experiments, applies statistical methods to web-scale data, and collaborates with data, product, growth, and engineering teams. The role also communicates complex findings to diverse audiences and helps foster a strong data science culture.

Leads AI enablement for Global Quality by translating business and quality-process needs into prototypes, requirements, validation plans, and scalable governed solutions. Builds AI-enabled decision support, retrieval, workflow automation, dashboards, and agentic tools; defines data, knowledge, testing, and governance requirements; manages vendors; and partners with technology, data, cybersecurity, and Quality teams. The role also develops executive presentations, business cases, roadmaps, process maps, and handoff packages for enterprise adoption.

Build and ship applied AI tools for legal work: identify high-impact opportunities, prototype agents and retrieval systems, integrate with enterprise data and systems, measure quality against human baselines, and train and onboard lawyers. Partner with Legal Ops, IT, Security, and product teams to iterate, evaluate, and scale AI-native legal capabilities across Match Group.

Own and optimize the inference stack for cost, throughput, latency, and reliability. Responsibilities include KV-cache management, batching, quantization, speculative decoding, long-context optimization, provider routing, serving-engine optimization, GPU and kernel tuning, and profiling systems. The role requires production expertise with modern inference engines, model serving, GPU performance, and systems programming.

Supports enterprise AI and digital transformation initiatives through AI solution development, AI agent configuration, data analytics, automation, governance, user training, and proof-of-concept projects. The intern will evaluate AI use cases, analyze adoption metrics, document responsible AI practices, test automated solutions, and help promote AI adoption within a regulated financial services environment.

Own product-led growth analytics across activation, engagement, conversion, monetization, and retention. Lead experimentation, statistical analysis, causal inference, metric development, and proactive opportunity discovery. Build scalable analytical systems, monitoring frameworks, semantic models, and AI-powered tools. Partner with Product, Engineering, and leadership to shape product bets, investment decisions, and roadmap priorities. Work autonomously in a remote-first environment and communicate complex findings through clear, actionable recommendations.

Build predictive and machine learning models for patient needs, operational forecasting, churn, advocate quality, and risk. Design A/B tests and causal inference studies, apply advanced statistical methods, productionize models with data engineers, and communicate insights to business stakeholders. The role requires Python, advanced SQL, forecasting expertise, statistical proficiency, data privacy awareness, and the ability to work independently in a fast-paced healthcare startup.

Lead fraud operations automation by building AI agents and LLM workflows, identifying manual investigative processes, and converting them into auditable automated workflows. Provide analytical insights into suspicious merchants and customers, develop prioritization methods, and maintain operational dashboards and KPIs. Partner with Fraud Strategy, Product, Data, and Engineering teams to improve fraud policies, models, case management, and investigator effectiveness.