Senior Machine Learning Engineer, Cloudflare
Web performance and security platform
Be an early applicant
Web performance and security platform
1001+ employees
Be an early applicant
1001+ employees
Company mission
To help build a better Internet that is bold and ambitious.
Role
Who you are
- 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
- 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
- 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
- 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
- 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
What the job involves
- The Data Intelligence & Analytics organization builds the core data platform and internal products that power decision-making across the company
- We design and operate large-scale data systems, own the company’s data lake, ingestion infrastructure, and platform tooling, and develop end-to-end applications that transform complex datasets into fast, reliable, business-critical products used daily by go-to-market, product, and engineering teams
- Our work sits at the intersection of data platforms, distributed systems, and product development, giving engineers the opportunity to own meaningful problems across the stack and build systems that truly run the business
- We are looking for a visionary and hands-on Lead Machine Learning Engineer to join our Austin team
- N this role, you will be the principal architect behind the next generation of our unified AI/ML platform, designing and building the infrastructure that powers everything from traditional predictive models to generative AI, large language models (LLMs), and autonomous agent frameworks
- You will own the end-to-end technical strategy, blueprint, and execution of scalable backend services and data pipelines that support AI-driven applications across go-to-market, engineering, and product teams
- Because our products are initiated and owned entirely within the team, you will drive the vision from initial requirements and system design to global deployment, optimization, and long-term evolutionary ownership
- 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).\
Application process
- Please note that applicants who progress to the offer stage of the interview process may be asked to attend an in-person interview within one of the Cloudflare Offices or Cloudflare Hubs. More details about this will be available at that stage of the interview process
Our take
Cloudflare is a multi-service global network that provides web security and infrastructure, DDoS mitigation services, and a content delivery network. Founded in 2009, Cloudflare has risen to become an industry giant, with over 25 million global internet properties on its network.
Cloudflare has been responsible for a number of admirable initiatives, offering free web protection to human rights groups, journalists, artists, and US election websites. This goes some way to counteracting the flak Cloudflare has received in some circles for the controversial groups and users who use its services. This chequered history, however, has not affected its over 80% market share in the content delivery network field.
This is perhaps in part because it has proven sharp at staying abreast of the rapidly morphing tech and digital space and customer demands. For example, Cloudflare is shifting to 100% renewable energy usage to create a zero-emissions internet, and in 2020 released a feature that would help users navigate data privacy regulations by selecting where their data is stored.
And impressive third-quarter 2025 results show strong momentum, with revenue rising to $562 million from $430 million a year earlier. The company also raised its full-year guidance, underscoring confidence in continued growth. As demand for secure, cloud-based infrastructure keeps growing, Cloudflare's expanding platform and improving efficiency leave it well positioned to capture further gains in a rapidly growing market.
Steph
Company Specialist at Welcome to the Jungle
Insights
16% employee growth in 12 months
Company
Company benefits
- Competitive pay: We offer a competitive total rewards package, where every employee is an owner of our stock.
- Take-what-you-need vacation: We encourage employees to find a comfortable work-life balance by taking as many days off as they need while still being able to perform their jobs satisfactorily. (We really mean it!)
- Paid maternity & paternity leave: Our global parental leave policy allows up to sixteen paid weeks of bonding leave time for all qualifying new parents.
- Employee benefits: We offer a comprehensive benefits package including healthcare, life insurance, short- and long-term disability, pension plans in accordance with the market practice in our locations.
- Commuter program: We are flexible about where we work and value connecting at the office. Our commuter benefits program is in place to support team members' commute to work via public transportation without the extra cost burden.
- Wellbeing: Global offerings that provide mental health, childcare and family forming support to our employees across our offices.
Company HQ
China Basin, San Francisco, CA
Leadership
Lee Holloway
(Co-Founder & Lead Engineer)Lee began their career as an Engineer at Homewarehouse for a year before working at Unspam Technologies for 4 years. They co-created Project Honey Pot in 2004 and Cloudflare in March 2009, serving as Lead Engineer of both to present.
Michelle Zatlyn
(Co-Founder, President & Co-Chair)Having worked for Investor Economics and I Love Rewards for a combined 4 years, Michelle worked at Toshiba for 3 years as a Product Manager. She then co-founded Cloudflare in 2009 as COO, and has served as President since 2020. She is also a Board Member at Atlassian.
Matthew Prince
(Co-Founder & CEO)Matthew studied for an MBA at Harvard Business School before co-founding Unspam Technologies in December 2001, and Cloudflare in 2009, where he has served as CEO ever since.
Salary benchmarks
We don't have enough data yet to provide salary benchmarks for this role.
Submit your salary to help other candidates with crowdsourced salary estimates.
Share this job
View 95 more jobs at Cloudflare