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Staff Machine Learning Infrastructure Engineer

New York, New York, United States

StubHub is on a mission to redefine the live event experience on a global scale. Whether someone is looking to attend their first event or their hundredth, we’re here to delight them all the way from the moment they start looking for a ticket until they step through the gate. The same goes for our sellers. From fans selling a single ticket to the promoters of a worldwide stadium tour, we want StubHub to be the safest, most convenient way to offer a ticket to the millions of fans who browse our platform around the world.

About the Opportunity

We're seeking an accomplished Staff Machine Learning Infrastructure Engineer to join StubHub's Data Engineering & Analytics team as a high-impact individual contributor focused on machine learning infrastructure and real-time inference systems. You'll architect and build the foundational ML platforms that power recommendation systems, pricing optimization, and personalization across StubHub's product. 

As a Staff-level IC, you'll operate as a technical force multiplier, setting technical direction for ML infrastructure across the organization. You'll lead through influence rather than management, advocating for long-term technical progress while balancing organizational needs. Your work will span strategic initiatives measured in months and years, focusing on high-leverage technical decisions that enable entire teams to be more effective. 

Location: Hybrid (3 days in office/2 days remote) – New York, NY or Los Angeles, CA 
 
Strategic Need
We have increasing needs to scale our machine learning capabilities to power personalized experiences, dynamic pricing, and intelligent recommendations across our platform. Our current ML infrastructure requires modernization to support real-time inference at scale, improve feature engineering workflows, and enable faster model deployment and iteration cycles. Additionally, we need to create the foundational data model along with the corresponding data pipelines, and build shared tooling to ease the process of developing and operating high quality trustworthy data assets. 
 
What You'll Do

As a Staff Enginner focused on ML Infrastructure, you'll work across four key dimensions: 

Setting Technical Direction 

  • Architect ML infrastructure strategy that aligns technical approaches across Data Science, ML Engineering, and Platform teams 
  • Drive consensus on technical vision for feature stores, inference services, and model lifecycle management 
  • Advocate for long-term technical progress while balancing immediate organizational needs 
  • Establish architectural patterns that become standards across StubHub's ML ecosystem 

Core ML Infrastructure & Exploration 

  • Prototype and investigate ambiguous, high-impact ML infrastructure problems 
  • Build production-grade inference services with sub-100ms latency, intelligent caching, and 99.9% uptime SLAs 
  • Design model lifecycle management systems including versioning, A/B testing, rollback capabilities, and performance monitoring 
  • Modernize recommendation systems from legacy SQS-based architecture to scalable, real-time streaming solutions 
  • Explore innovative solutions outside standard approaches for complex ML infrastructure challenges 

Technical Leadership & Mentorship 

  • Provide engineering perspective in high-level organizational discussions about ML strategy 
  • Mentor engineers across the platform, actively sponsoring promising team members 
  • Inject technical context into critical decision-making processes 
  • Lead complex technical initiatives spanning multiple teams and quarters 

Being the "Glue" 

  • Connect different team efforts to ensure ML infrastructure initiatives succeed 
  • Handle behind-the-scenes work that keeps critical ML projects moving forward 
  • Expedite high-priority ML infrastructure needs across the organization 
  • Ensure important strategic work gets completed even when it spans team boundaries 

 

What You've Done

  • 8+ years of relevant software or data engineering development experience in a fast-paced, high growth environment 
  • 3+ years of experience with machine learning infrastructure, MLOps, or ML platform engineering 
  • Proven track record of setting technical direction and leading complex, multi-team initiatives 
  • Strong programming and analytical ability with expertise in Python, Scala, or Java, and infrastructure-as-code 
  • Experience with feature store services and how they interoperate between batch and live inference systems 
  • Experience with live inference services including caching, SLAs, and performance optimization for production ML workloads 
  • Experience with model lifecycle management including versioning, A/B testing, and rollback capabilities 
  • Experience with streaming systems (Spark, Kafka) and how they relate to overarching ML platform architecture 
  • Experience with cloud-based ML platforms such as AWS SageMaker, Google Vertex AI, or Azure ML 
  • Experience mentoring engineers and establishing technical best practices across teams 

Staff-Level Capabilities 

  • Technical leadership through influence rather than formal management authority 
  • Strategic thinking with ability to balance long-term technical vision with immediate organizational needs 
  • Cross-functional collaboration skills to work effectively with Data Science, Product, and Engineering teams 
  • Communication skills to inject technical context into high-level organizational discussions 
  • Problem-solving approach for ambiguous, high-impact technical challenges 
  • Mentorship and sponsorship experience growing junior and mid-level engineers 

Nice to Have 

  • Experience with real-time recommendation systems and personalization platforms at scale 
  • Knowledge of ML model serving frameworks (TensorFlow Serving, TorchServe, Seldon, etc.) 
  • Experience with A/B testing frameworks and experimentation platforms 
  • Experience with distributed computing frameworks (Ray, Dask, etc.) 
  • Knowledge of ML security and privacy considerations 
  • Track record of technical writing or speaking at conferences about ML infrastructure 
 
What We Offer:
  • Accelerated Growth Environment: Immerse yourself in an environment designed for swift skill and knowledge enhancement, where you have the autonomy to lead experiments and tests on a massive scale.
  • Top Tier Compensation Package: Enjoy a rewarding compensation package that includes enticing stock incentives, aligning with our commitment to recognizing and valuing your contributions.
  • Flexible Time Off: Embrace a healthy work-life balance with unlimited Flex Time Off, providing you the flexibility to manage your schedule and recharge as needed.
  • Comprehensive Benefits Package: Prioritize your well-being with a comprehensive benefits package, featuring 401k, and premium Health, Vision, and Dental Insurance options.

The anticipated gross base pay range is below for this role. Actual compensation will vary depending on factors such as a candidate’s qualifications, skills, experience, and competencies. Base annual salary is one component of StubHub’s total compensation and competitive benefits package, which includes equity, 401(k), paid time off, paid parental leave, and comprehensive health benefits. 

Salary Range

$300,000 - $350,000 USD

About Us 
StubHub is the world’s leading marketplace to buy and sell tickets to any live event, anywhere. Through StubHub in North America and viagogo, our international platform, we service customers in 195 countries in 33 languages and 49 available currencies. With more than 300 million tickets available annually on our platform to events around the world -- from sports to music, comedy to dance, festivals to theater -- StubHub offers the safest, most convenient way to buy or sell tickets to the most memorable live experiences. Come join our team for a front-row seat to the action. 
 
For California Residents: California Job Applicant Privacy Notice found here
 
We are an equal opportunity employer and value diversity on our team. We do not discriminate on the basis of race, color, religion, sex, national origin, gender, sexual orientation, age, disability, veteran status, or any other legally protected status.

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