Top AI & Machine Learning Jobs

Sell and shape adoption of CoreWeaves AI Runtime Services by driving technical-commercial strategy, proving value with early customers, creating playbooks, and feeding product roadmap. Advise on serving frameworks, batching, execution isolation, throughput modeling, GPU utilization, and SLA structures while partnering with product, infra, and sales teams.

Lead marketing measurement and experimentation to quantify attribution and incrementality, optimize ad platforms and CRM, build performance data assets, analyze lifecycle engagement, and communicate insights to influence marketing strategy and spend allocation.

Lead product strategy and execution for agentic AI and LLM-driven operational intelligence. Define use cases, prototype with agent frameworks, partner with AI/ML and engineering on architecture, safety, evaluation, and MLOps, drive 0->1 SaaS feature development, run discovery and A/B tests, and enable customer adoption and go-to-market.

Lead product manager to define strategy and deliver agentic AI, LLM-driven workflows, and autonomous operational agents. Translate customer problems into technical requirements (LLMs, RAG, agent orchestration), prototype with agent frameworks, own roadmap/PRDs, partner with AI/ML and engineering on grounding, safety, evaluation, and MLOps, and drive adoption through go-to-market, onboarding, and measurement.

Design, build, and deploy AI-powered observability solutions using LLMs and RAG. Develop NLP and generative AI features for incident reports, semantic search, and context-aware retrieval. Optimize models for cloud-native, scalable deployments and collaborate with product, engineering, and data science teams to integrate AI into the platform.

Lead and grow a data science team building predictive models for credit risk and payments. Drive integration with product and engineering, run quarterly planning, mentor scientists, adopt AI-assisted and agentic workflows, and collaborate on model infrastructure and deployment.

Lead and grow a data science team to build predictive models for credit risk and payments; mentor scientists; integrate DS work with product, engineering, and operations; run quarterly planning with stakeholders; drive adoption of AI-assisted and agentic workflows with validation and quality standards; and collaborate with modeling engineering to improve model research, development, and deployment infrastructure.

Design, deploy, and operate scalable data pipelines and cloud infrastructure for ML/GenAI, manage day-2 LLM serving platforms, automate IaC and platform maintenance, ensure observability and reliability, and support production ETL and incident response.

Design, develop, and deploy ML models for KYC/KYB, AML, and fraud detection; reduce chargebacks; analyze large datasets for identity and regulatory risk; run A/B tests; build data pipelines and real-time scoring with Python and SQL; collaborate with product, engineering, and compliance to translate requirements into automated solutions.

Design and scale data pipelines and ML/LLM systems, build agentic automation for pipeline generation and maintenance, improve data monitoring, and collaborate with analysts, product, and ML teams to deliver reliable end-to-end data and AI infrastructure for a high-growth e-commerce platform.

Build scalable backend applications in Python, create and maintain APIs, work with MSSQL and large datasets, implement AI features (chat, QA, summarization), use embeddings and vector search, optimize database performance, debug and improve system performance, and collaborate on responsible AI solutions.

Lead enterprise architecture strategy and roadmaps for large-scale, secure, cloud-based systems. Drive solution design across data, AI/ML, platforms, integration, governance, and stakeholder alignment to productionize scalable intelligent solutions.

Build, deploy, and maintain production ML and AI systems (including LLMs, RAG, similarity search, guardrails, observability). Partner with cross-functional teams to design data pipelines, fine-tune models, monitor performance, and apply Responsible/Explainable AI best practices while contributing technical vision and roadmap for risk-focused AI products.

Develop and maintain econometric time-series and behavior models for balance-sheet portfolios. Apply quantitative methods and machine learning to improve business performance, validate and backtest models, document methods, and communicate results to management, regulators, and model risk teams.

Lead enterprise-scale Generative AI programs from research to production, focusing on multi-agent conversational systems, agentic architectures, pretraining/finetuning, and inference optimization. Drive cross-functional delivery, manage technical risk, establish evaluation and deployment frameworks, partner with risk/compliance, and ensure scalable, secure adoption of AI solutions.