1 day ago
Base Salary
$209k - $225k/yr
Responsibilities
- Own the architecture of ranking and recommendation systems, including candidate retrieval, multi-objective ranking, calibration, and ensemble optimization.
- Lead offline evaluation and online experimentation to evolve optimization from short-term conversion toward long-term diner value.
- Bring information retrieval and recommender-system research into production through query understanding, embeddings, sequential user representations, and real-time inference.
- Raise standards for model evaluation, reproducible training pipelines, deployment safety, 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 grow the team’s technical depth.
- Drive cross-team design and architecture decisions and communicate model behavior, performance metrics, and trade-offs to technical, non-technical, and executive stakeholders.
Requirements
- MS or PhD in a quantitative discipline such as Computer Science, Math, Physics, Engineering, or Statistics, or equivalent experience.
- 6+ years building and shipping machine learning systems, including 3+ years operating at senior-level scope across teams, model families, or systems.
- 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 GPU-based runtime models.
- 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 feature pipelines.
- Experience designing A/B tests, selecting guardrails, and assessing whether offline metrics are reliable.
- Experience with cloud ML infrastructure such as AWS or SageMaker, model deployment, and production monitoring and observability.
- Demonstrated technical leadership through mentoring, architecture reviews, cross-team influence, and decision-making without direct authority.
- Strong communication skills and ability to synthesize research publications into production systems.
- Experience with language understanding over imperfect grammar, including real-world search queries and catalog text, is a strong plus.
- Demonstrated interest in continuous, self-motivated learning.
Benefits
- Hybrid work model requiring 3 days per week in the office, with the option and encouragement to work in the office up to 5 days per week.
- Competitive compensation package with equity and a 401(k).
- Medical, dental, and vision plan options, plus 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 as required by law.
- Paid parental leave, discounted meals, and exclusive perks across the Wonder family of brands; eligibility and options vary by classification and location.
Categories
About Wonder
Wonder builds a mealtime platform that lets customers mix dishes from multiple restaurant brands in a single order, with options for dine-in, delivery, pickup, and meal kits. The New York-based, privately held company owns and operates its kitchens and technology and also develops robotic cooking systems used with enterprise B2B clients. Wonder expanded its portfolio by acquiring meal-kit pioneer Blue Apron in 2023.