1 day ago
Base Salary
$209k - $217k/yr
Responsibilities
- Own the architecture of the ranking and recommendation stack, including candidate retrieval, multi-objective ranking, calibration, and conversion-versus-profitability optimization.
- Lead offline evaluation and online experimentation to validate changes to ranking and recommendation objectives.
- Apply information retrieval, recommender systems, LLM-driven query understanding, embeddings, sequential user representations, and real-time inference in production.
- Raise engineering and operational 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 drive design and architecture reviews across teams.
- Translate model behavior, performance metrics, and technical trade-offs for technical, non-technical, product, 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.
- 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-based 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 evaluating the limitations of offline metrics.
- Experience with cloud ML infrastructure such as AWS/SageMaker or equivalent, model deployment, and production monitoring and observability.
- Demonstrated technical leadership through mentoring, cross-team architecture reviews, and influence without direct authority.
- Strong communication skills for explaining performance metrics, model behavior, and technical trade-offs to technical and non-technical audiences.
- Ability to follow current research publications and translate relevant research into production systems.
- Experience with language understanding over imperfect grammar is a strong plus, along with a strong interest in continuous 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.
- Company-paid short- and long-term disability coverage.
- Paid time off, including 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.
- Benefits eligibility, effective dates, and plan options vary by employment 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.