Top AI & Machine Learning Jobs

Develop and scale predictive, machine learning, deep learning, and generative AI models and applications. Partner with business, product, engineering, and data teams to identify challenges, improve data quality, deliver scalable model insights, and support revenue and growth goals. Build AI agents, chatbots, and model-serving workflows using modern data and AI infrastructure. Present findings clearly, identify significant trends, and help hire, mentor, and establish best practices for the Bengaluru data science team.

Build and operate production AI agents on Cloudflare’s edge platform using Workers, Durable Objects, storage services, model tooling, and agent frameworks. Responsibilities include tool integration, semantic memory, evaluations, guardrails, observability, security boundaries, human escalation, event processing, and performance optimization. The role requires end-to-end ownership, production operations, and on-call support for globally deployed customer-facing agents.

Architect and build resilient, high-throughput services for agentic web discovery, content transformation, indexing, tool calling, and generative interfaces. Define emerging agent protocols and technical standards, write architectural specifications, shape product roadmaps, improve observability and reliability, lead incident response, and mentor engineers. Partner with product teams, customers, and stakeholders while driving technical governance, hiring, and engineering process improvements across Cloudflare.

Lead research, design, and evaluation of large-scale detection models for automated, fraudulent, and malicious activity. Define measurement frameworks for noisy or incomplete ground truth, mentor data science practitioners, advance AI/ML techniques, and shape long-term detection strategy. Partner with ML Engineering, Data Engineering, and Product teams to influence roadmaps and protect Cloudflare customers from evolving threats.

Lead productionization and optimization of ML models for a global, serverless inference platform. Build benchmarking/evaluation tooling, improve inference performance (quantization, compilation, batching), integrate models with heterogeneous GPUs and accelerators, enhance deployment workflows and observability, and mentor engineers while driving ML production best practices.

Design and build AI Gateway systems for high-volume, latency-sensitive inference at the edge. Implement APIs, routing, observability, controls, developer workflows and tooling. Prototype ambiguous ideas to production, improve reliability, performance, cost-efficiency and debuggability, and mentor engineers while dogfooding AI-native tools, agents, and automation to accelerate development and operations.

Build and operate production AI agents on Cloudflare’s edge platform using Workers, Durable Objects, model tooling, vector databases, queues, and observability systems. Develop agent memory, tool integrations, evaluations, guardrails, security boundaries, dashboards, and distributed tracing. Own deployments, performance, cost optimization, reliability, and on-call support while delivering customer-facing workflows that summarize issues, propose fixes, and escalate to humans.

Build scalable, reliable ML and GenAI models and backends; partner with product, engineering, and business stakeholders; improve data collection and quality with data engineers; train and deploy models at scale; publish model insights; analyze trends with statistical significance; present findings to varied audiences; and hire, mentor, and grow the Austin data science team.

Build and maintain Cloudflare's new vector database offering: design, implement, and operate distributed, low-latency cloud systems. Own features across the stack, ensure reliability and performance, write design documents, and apply machine learning and data retrieval expertise using TypeScript, Go, Rust, and related technologies.

Lead design and build of a scalable, multi-tenant AI/ML platform powering traditional ML, LLMs, and agentic systems. Own end-to-end architecture, production-grade backends, orchestration, LLMOps, vector DB integration, and cross-functional delivery while enforcing engineering standards, security, and observability.