Senior Machine Learning Engineer at Yahoo

Job description

Yahoo Mail is the ultimate consumer inbox with hundreds of millions of users. It’s the best way to access your email and stay organized from a computer, phone or tablet. With its beautiful design and lightning fast speed, Yahoo Mail makes reading, organizing, and sending emails easier than ever. A Little About Us Yahoo Mail is the ultimate Consumer Inbox with over 220 million monthly active users. We empower users to run the business of their lives through a fast, intuitive platform while managing billions of daily inbound connections and petabytes of efficient storage. Our engineering team is actively transitioning to a 100% native public cloud architecture. The Mail Intelligence Org develops intelligent capabilities at scale to uncover user interests, reveal habits, and personalize user journeys across Yahoo Mail and the broader Yahoo ecosystem. We manage billions of messages using advanced backend systems and state-of-the-art AI - including NLP, Generative AI, and autonomous agentic workflows. We are a team of passionate engineers dedicated to high coding standards, world-class architecture, and an owner’s mindset. A Lot About You AI-First Mindset: AI is core to how you work, innovate, and deliver results. You actively experiment with advanced tools (e.g., Claude, Cursor, Codex), integrate them into your daily development workflows, and measure success by how effectively you leverage AI to solve complex problems. Agentic Thinker: You have hands-on experience designing, orchestrating, and evaluating agentic systems (multi-step reasoning, tool integration, memory, autonomous workflows, and LLM-based agents) in production environments. Engineering Standards: You take extreme ownership of your work, bringing high engineering rigor, clean code practices, and a focus on building scalable, fault-tolerant architectures. Collaborative Partner: You thrive in cross-functional, global engineering environments, working closely with Tech Leads, Architects, and Engineers to deliver robust features. Continuous Learner: You stay on the cutting edge of rapid advancements in machine learning, generative models, and infrastructure trends, continuously bringing novel approaches into practice. Responsibilities Architect & Personalize: Design, build, and deploy high-throughput AI/ML capabilities that power real-time personalization and deep insights for hundreds of millions of users. GenAI & Agentic Systems: Spearhead the implementation and orchestration of production LLM-backed applications and agentic workflows (prompt engineering, tool integration, and automated feedback loops). AI-Driven Execution: Embed AI-first practices into daily development cycles using AI-assisted tools to accelerate iteration, improve code quality, and modernize engineering workflows. Data-Driven Intelligence: Process petabyte-scale data streams using big data processing and ML techniques to derive key insights from mail metadata and content. Inference at Scale: Deploy and optimize ML/LLM models for real-time production serving using modern frameworks (e.g., vLLM, TensorRT-LLM, Triton, ONNX Runtime). Resilience & Fallback Design: Implement automated feedback loops and graceful recovery paths to handle model failure modes smoothly, maintaining high service availability and user satisfaction. Tradeoff Management: Balance compute costs, latency, quality, and model performance to optimize systems for massive consumer scale. Qualifications Education: Bachelor’s degree in Computer Science, Data Science, AI, or a related field; or, equivalent experience. Experience: 5 years of professional experience engineering and deploying production machine learning or data science systems at scale. Technical Proficiency: Proficient in Python or Java, with deep hands-on expertise in standard ML frameworks (PyTorch, TensorFlow, Hugging Face, Scikit-learn). Generative & Agentic AI: Demonstrated experience building LLM-based applications or agentic systems (multi-step reasoning, tool usage, RAG, or autonomous workflows). High-Performance Inference: Experience deploying and serving models in production using tools like vLLM, TensorRT-LLM, Triton Inference Server, or ONNX Runtime. Big Data Handling: Hands-on experience processing large datasets using Spark, Hadoop, or cloud-native data pipelines. Communication: Excellent verbal and written communication skills for effective collaboration with cross-functional and global engineering teams. Preferred Qualifications Cloud Infrastructure: Direct experience building and scaling ML pipelines on Google Cloud Platform (GCP) or similar native public cloud environments. Domain Expertise: Prior experience working with email systems, large-scale consumer web platforms, NLP, or search architecture. AI Tooling: Early adoption and proactive integration of modern developer tooling (Cursor, Claude, Codex) directly into daily development practices. The material job duties and responsibilities of this role include those listed above as well as adhering to Yahoo policies ; exercising sound judgment ; working effectively, safely and inclusively with others ; exhibiting trustworthiness and meeting expectations ; and safeguarding business operations and brand integrity. At Yahoo, we offer flexible hybrid work options that our employees love! While most roles don’t require regular office attendance, you may occasionally be asked to attend in-person events or team sessions. You’ll always get notice to make arrangements. Your recruiter will let you know if a specific job requires regular attendance at a Yahoo office or facility. If you have any questions about how this applies to the role, just ask the recruiter! Yahoo is proud to be an equal opportunity workplace. All qualified applicants will receive consideration for employment without regard to, and will not be discriminated against based on age, race, gender, color, religion, national origin, sexual orientation, gender identity, veteran status, disability or any other protected category. Yahoo will consider for employment qualified applicants with criminal histories in a manner consistent with applicable law. Yahoo is dedicated to providing an accessible environment for all candidates during the application process and for employees during their employment. If you need accessibility assistance and/or a reasonable accommodation due to a disability, please submit a request via the Accommodation Request Form ( www.yahooinc.com/careers/contact-us.html ) or call 1.866.772.3182 . Requests and calls received for non-disability related issues, such as following up on an application, will not receive a response. We believe that a diverse and inclusive workplace strengthens Yahoo and deepens our relationships. When you support everyone to be their best selves, they spark discovery, innovation and creativity. Among other efforts, our 11 employee resource groups (ERGs) enhance a culture of belonging with programs, events and fellowship that help educate, support and create a workplace where all feel welcome. The compensation for this position ranges from $128,250.00 - $266,875.00/yr and will vary depending on factors such as your location, skills and experience.The compensation package may also include incentive compensation opportunities in the form of discretionary annual bonus or commissions. Our comprehensive benefits include healthcare, a great 401k, backup childcare, education stipends and much (much) more. Currently work for Yahoo? Please apply on our internal career site.

Requirements

Must-haves

A Little About Us
A Lot About You
Engineering Standards: You take extreme ownership of your work, bringing high engineering rigor, clean code practices, and a focus on building scalable, fault-tolerant architectures.
Collaborative Partner: You thrive in cross-functional, global engineering environments, working closely with Tech Leads, Architects, and Engineers to deliver robust features.
Continuous Learner: You stay on the cutting edge of rapid advancements in machine learning, generative models, and infrastructure trends, continuously bringing novel approaches into practice.
Architect & Personalize: Design, build, and deploy high-throughput AI/ML capabilities that power real-time personalization and deep insights for hundreds of millions of users.
GenAI & Agentic Systems: Spearhead the implementation and orchestration of production LLM-backed applications and agentic workflows (prompt engineering, tool integration, and automated feedback loops).
AI-Driven Execution: Embed AI-first practices into daily development cycles using AI-assisted tools to accelerate iteration, improve code quality, and modernize engineering workflows.
Data-Driven Intelligence: Process petabyte-scale data streams using big data processing and ML techniques to derive key insights from mail metadata and content.
Inference at Scale: Deploy and optimize ML/LLM models for real-time production serving using modern frameworks (e.g., vLLM, TensorRT-LLM, Triton, ONNX Runtime).
Resilience & Fallback Design: Implement automated feedback loops and graceful recovery paths to handle model failure modes smoothly, maintaining high service availability and user satisfaction.
Tradeoff Management: Balance compute costs, latency, quality, and model performance to optimize systems for massive consumer scale.
Education: Bachelor’s degree in Computer Science, Data Science, AI, or a related field; or, equivalent experience.
Experience: 5 years of professional experience engineering and deploying production machine learning or data science systems at scale.
Technical Proficiency: Proficient in Python or Java, with deep hands-on expertise in standard ML frameworks (PyTorch, TensorFlow, Hugging Face, Scikit-learn).
Generative & Agentic AI: Demonstrated experience building LLM-based applications or agentic systems (multi-step reasoning, tool usage, RAG, or autonomous workflows).
High-Performance Inference: Experience deploying and serving models in production using tools like vLLM, TensorRT-LLM, Triton Inference Server, or ONNX Runtime.
Big Data Handling: Hands-on experience processing large datasets using Spark, Hadoop, or cloud-native data pipelines.
Communication: Excellent verbal and written communication skills for effective collaboration with cross-functional and global engineering teams.

Nice-to-haves

Cloud Infrastructure: Direct experience building and scaling ML pipelines on Google Cloud Platform (GCP) or similar native public cloud environments.
Domain Expertise: Prior experience working with email systems, large-scale consumer web platforms, NLP, or search architecture.
AI Tooling: Early adoption and proactive integration of modern developer tooling (Cursor, Claude, Codex) directly into daily development practices.
Currently work for Yahoo? Please apply on our internal career site.

Application prep

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Keywords to mirror

aillmmachine learningmlpythonjavagcpaccessibility

Platform: workday
Effort: long

Benefits

Benefits detected in the listing.

401k