Senior Machine Learning Engineer

Навыки

• AI-агенты
• AI-инструменты в работе
• CI/CD
• Data Lake
• Распределённые системы
• Docker
• ETL / ELT

О компании и продукте

• At Cloudflare, we are on a mission to help build a better Internet. Today the company runs one of the world’s largest networks that powers millions of websites and other Internet properties for customers ranging from individual bloggers to SMBs to Fortune 500 companies. Cloudflare protects and accelerates any Internet application online without adding hardware, installing software, or changing a line of code. Internet properties powered by Cloudflare all have web traffic routed through its intelligent global network, which gets smarter with every request. As a result, they see significant improvement in performance and a decrease in spam and other attacks. Cloudflare was named to Entrepreneur Magazine’s Top Company Cultures list and ranked among the World’s Most Innovative Companies by Fast Company.
• At Cloudflare, we’re not looking for people who wait for a polished roadmap; we’re looking for the builders who see the cracks in the Internet that everyone else has simply learned to live with. We value candidates who have the instinct to spot a "normalized" problem and the AI-native curiosity to create a solution using the latest tools. Our culture is built on iteration, leveraging AI to ship faster today to make it better tomorrow, while ensuring that every improvement, no matter how small, is shared across the team to lift everyone up. If you’re the type of person who values curiosity over bureaucracy, and that AI is a partner in solving tough problems to keep the Internet moving forward, you’ll fit right in.
• Available Locations: Austin, TX - Hybrid

Задачи

• Architect and evolve a highly scalable, multi-tenant AI/ML platform that seamlessly unifies traditional ML (classification, regression, forecasting) and Generative AI/LLM orchestration
• Design and implement robust production-grade AI Agents and Advanced Chatbots
• Build reliable execution environments for Multi-Agent Systems, including state management, long-term memory architectures, and Model Context Protocol (MCP) server integrations
• Build high-throughput, low-latency application backends and orchestration layers
• Partner closely with data, platform, and full-stack engineers to ensure seamless feature delivery and reliable production operations
• Act as a technical anchor for the Data Science team – enforcing rigorous engineering standards, leading design and security reviews, evaluating build-vs-buy decisions, and mapping business requirements to robust technical designs
• Evaluate trade-offs and drive adoption of modern AI infrastructure tools, optimized embedding pipelines, vector databases, and serverless compute paradigms (such as Workers AI).\

Требования

• Extensive experience as a Senior or Lead ML Engineer, with a proven track record of architecting and operating production-grade ML platforms, services and distributed backends
• Strong competency in Traditional ML lifecycles (feature stores, training pipelines, model monitoring) alongside deep experience in Generative AI patterns (RAG pipelines, context engineering, fine-tuning, guardrailing, and agentic AI systems)
• Mastery of Python and robust experience with modern backend ecosystems
• Familiarity with (or willingness to collaborate on) full-stack technologies like React and TypeScript is highly valued
• A builder's mindset
• You are comfortable navigating ambiguity, shaping your own technical roadmap, adapt as needed and taking extreme ownership of system reliability, costs, and model performance
• Examples of desirable skills, knowledge and experience
• Technical Leadership & Systems Architecture
• 3+ years of dedicated ML Engineering experience within a large-scale, enterprise environment (handling petabyte-scale data and working across globally distributed teams)
• Proven ability to architect, scale, and secure reliable, highly observable distributed systems, with a track record of leveling up platform foundations
• Experience mentoring engineers, leading by example through high-quality code and rigorous design reviews, and fostering a culture of technical excellence
• Strong problem-solving skills with a demonstrated ability to independently drive complex projects through ambiguous spaces and collaborate cross-functionally with data engineers, full-stack teams, and analysts
• AI, LLMOps & Agentic Engineering
• Hands-on proficiency in building production-grade GenAI applications and multi-agent systems using advanced LLM frameworks like LangGraph, LangChain, or Autogen
• Deep understanding of agent harness primitives, state management, memory architectures, and tool-calling loop mechanics
• Experience establishing LLMOps foundations, including automated prompt tracking, LLM evaluation pipelines (e.g., Ragas, TruLens), vector database optimization, context/token management, and real-time guardrailing/moderation layers
• Deep experience in scientific computing using Python (Scikit-Learn, PyTorch, or TensorFlow) and deploying traditional systems for end-to-end training, batch/real-time inference, and model observability
• Infrastructure, Cloud & Data Platforms
• Strong experience with Docker and Kubernetes for containerization and orchestration, alongside Infrastructure-as-Code tools like Terraform and public cloud ecosystems (GCP, AWS, or Azure)
• Hands-on experience with modern MLOps platform tools (e.g., Airflow, Argo Workflows, ArgoCD) and data systems including BigQuery, Postgres, and robust ETL/ELT practices
• Experience with full-stack web technologies and serverless/edge environments (FastAPI, TypeScript/JavaScript, Cloudflare Workers), with the agility to contribute across a multi-language stack
• Strong foundation in continuous integration/continuous deployment (CI/CD), testing frameworks (Pytest), and robust version control practices
• Education & Communication
• M.S. or Ph.D. in Computer Science, Statistics, Mathematics, or a related quantitative field
• Exceptional written and verbal communication skills, with the ability to translate complex technical architectures into clear concepts for both engineering peers and business stakeholders

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