1 day ago
Base Salary
$209k - $225k/yr
Responsibilities
- Own the architecture of the ranking and recommendation stack across candidate retrieval, multi-objective ranking, calibration, and ensemble optimization.
- Lead offline evaluation and online experimentation for evolving optimization objectives from short-term conversion toward long-term diner value.
- Bring information retrieval and recommender-systems research into production through query understanding, embeddings, sequential representations, and real-time inference.
- Set standards for model evaluation, scorecards, reproducible training pipelines, safe deployment, SLOs, observability, and technical debt management.
- Partner with Product, Search Engineering, Ads, and Data Platform to shape roadmaps and ensure required data and infrastructure are available.
- Mentor senior and mid-level engineers, participate in hiring, and raise technical depth across the organization.
- Drive architecture and design reviews across teams and communicate technical trade-offs to technical, non-technical, and executive stakeholders.
Requirements
- MS or PhD in Computer Science, Mathematics, Physics, Engineering, Statistics, or another quantitative discipline, or equivalent experience.
- At least 6 years building and shipping machine learning systems, including at least 3 years operating at senior-level scope.
- Deep production experience with recommendation systems, ranking, or information retrieval at scale under latency and cost constraints.
- Production deep-learning experience with TensorFlow or PyTorch, including training, serving, and tuning models on GPUs.
- Experience with large language models and transformer architectures, including fine-tuning, embedding generation, and deployment in latency-sensitive applications.
- Strong data-engineering fundamentals with PySpark, Hive/SQL, the Python data stack, and debuggable feature pipelines.
- Fluency in experimentation, including A/B-test design, guardrail selection, and evaluating the limitations of offline metrics.
- Experience with cloud ML infrastructure such as AWS or SageMaker, model deployment, production monitoring, and observability.
- Demonstrated technical leadership through mentoring, architecture reviews, cross-team influence, and decision-making without direct authority.
- Strong communication skills for explaining model behavior, performance metrics, and technical trade-offs to varied audiences.
- Ability to follow current research and translate relevant findings into production systems; experience with imperfect-grammar language understanding is a plus.
Benefits
- Hybrid work model requiring 3 days per week in the office, with the option and encouragement to work onsite up to 5 days per week.
- Competitive compensation package with equity and a 401(k).
- Medical, dental, and vision plan options.
- Company-paid short- and long-term disability coverage.
- Paid time off, flexible time off for exempt employees, paid vacation for non-exempt employees, and paid sick leave where required.
- Paid parental leave, discounted meals, and exclusive perks across the Wonder family of brands.
- Benefit eligibility, effective dates, and plan options vary by employment classification and location.
Categories
About Grubhub
Grubhub builds an online marketplace and logistics network for ordering takeout and delivery from local restaurants across the U.S. It earns commissions and delivery fees, and offers consumer subscriptions and corporate solutions. Founded in 2004 and headquartered in Chicago, it is part of Wonder Group and lists hundreds of thousands of merchants across 4,000+ cities.