Machine Learning Engineer

Wonder is hiring Machine Learning Engineers (Senior through Senior Staff) to take ownership of end-to-end machine learning architecture supporting search ranking, homepage and topic recommendations, retrieval, and query understanding. The role combines technical direction, rigorous evaluation, and cross-team delivery of personalized recommendation and retrieval systems.

Role Overview

You will define system-level design across ranking and recommendation workflows, guide the evolution of optimization objectives, and bring research advancements into production environments. This position also emphasizes improving engineering and operational standards across multiple teams while mentoring engineers and partnering closely with Product and engineering stakeholders.

Key Responsibilities

• Own the architecture of the ranking and recommendation stack end to end, including candidate retrieval, multi-objective ranking, calibration, and the ensemble that balances conversion against profitability.
• Make cross-system design decisions that require system-wide trade-offs beyond a single model owner.
• Lead the evolution of optimization objectives from short-term conversion toward long-term diner value, including offline evaluation and online experimentation to validate results prior to release.
• Integrate state of the art information retrieval and recommender systems into runtime, including LLM-driven query and intent understanding, embedding-based retrieval, sequential user representations for cold start, and real-time inference.
• Evaluate transfer of approaches to Wonder traffic and discontinue approaches that do not demonstrate value.
• Raise engineering and operational standards across teams through model evaluation and scorecards, reproducible training pipelines, safe deployment practices, SLOs and observability for tier-1 models, and proactive technical debt management.
• Partner with Product, Search Engineering, Ads, and the Data Platform to shape roadmaps, surface risks early, and ensure data and infrastructure readiness before model needs arise.
• Mentor senior and mid-level engineers, participate in hiring, and increase technical depth so critical systems are not dependent on a single person.
• Translate technical trade-offs into business terms for product and executive stakeholders and document rationales to preserve decision context over time.
• Continuously challenge assumptions and analyze business performance to identify improvements and innovation not yet pursued.

Required Qualifications

• MS/PhD in a quantitative discipline such as Computer Science, Math, Physics, Engineering, Statistics, or another technical field, or equivalent experience.
• 4-8+ years building and shipping machine learning systems.
• For staff-level roles: 3+ years operating at a senior-level scope, including setting technical direction across multiple teams, model families, or systems.
• Deep experience in recommendation systems, ranking, or information retrieval at scale in production with real latency and cost constraints.
• Production deep learning experience with TensorFlow or PyTorch, including training, serving, and tuning runtime models on GPUs.
• Experience with Large Language Models and transformer-based architectures, including fine-tuning, embedding generation, and deploying in latency-sensitive applications.
• Strong plus: experience with language understanding over imperfect grammar, including real-world search queries and menu or catalog text.
• Strong data engineering fundamentals including PySpark, Hive/SQL, the Python data stack, and feature pipelines that can be debugged and built.
• Fluency with experimentation, including designing A/B tests, selecting appropriate guardrails, and identifying when offline metrics mislead.
• Experience with cloud ML infrastructure such as AWS/SageMaker, model deployment, and production monitoring and observability.
• Demonstrated technical leadership through mentoring, driving architecture reviews beyond your own team, and influencing decisions without direct authority.
• Ability to communicate performance metrics, model behavior, and technical trade-offs to both technical and non-technical audiences, including executive stakeholders.
• Ability to keep up with research publications and synthesize them into production systems.
• Deep interest in self-directed continuous learning.

Technologies

• TensorFlow
• PyTorch
• GPUs
• Large Language Models
• Transformer-based architectures
• PySpark
• Hive/SQL
• Python
• AWS
• SageMaker
• A/B tests

Location and Compensation

• Location: New York, NY (onsite)
• Salary Range: USD 176,000 - 260,000 per year

Base salary by level (New York):

• Senior MLE: $176,000 - $191,000
• Staff MLE: $208,500 - $225,000
• Senior Staff MLE: $240,000 - $260,000

Benefits

• Competitive compensation package with equity
• 401(k)
• Choice of medical, dental, and vision plans
• Company paid short and long term disability coverage
• Paid time off including flexible time off for exempt employees
• Paid vacation for non-exempt employees
• Paid sick leave in compliance with applicable law
• Paid parental leave
• Discounted meals and exclusive perks across the Wonder family of brands

Additional Information

• Wonder participates in the federal government's E-Verify program to confirm employment eligibility.
• If you need an accommodation during the interview process, notify your recruiter.
• Interviews will be scheduled via email or text, and information about your candidacy will be shared accordingly.