Machine Learning Engineer

Wonder is building personalized search and recommendation experiences, and it needs Machine Learning Engineers who can own major parts of the system from architecture through evaluation and deployment. This role spans end-to-end work across ranking, retrieval, and query understanding, with a focus on raising the bar for both model quality and operational reliability.

As a Senior to Senior Staff Machine Learning Engineer, you will help set technical direction across teams, translate complex technical trade-offs into business terms, and mentor engineers so critical systems do not rely on a single person.

What you will own

• End-to-end architecture for the ranking and recommendation stack, including candidate retrieval, multi-objective ranking, calibration, and the ensemble that balances conversion against profitability.
• Cross-system design decisions that extend beyond the scope of a single model owner.
• Evolution of the optimization objective from short-term conversion toward long-term diner value, including offline evaluation and online experimentation to validate results before shipping.
• Production runtime integration of state-of-the-art methods for information retrieval and recommender systems, including:
  • LLM-driven query and intent understanding
  • Embedding-based retrieval
  • Sequential user representations for cold start
  • Real-time inference
• Rigorously assess what transfers to Wonder’s traffic and decline changes that do not hold up in practice.

How you will raise standards

• Raise engineering and operational standards across multiple teams, including model evaluation and scorecards, reproducible training pipelines, safe deployment, and SLOs with observability for tier-1 models.
• Proactively manage technical debt across systems before it becomes urgent.

Collaboration and leadership

• Partner with Product, Search Engineering, Ads, and the Data Platform to shape roadmaps, surface risks early, and ensure the right data and infrastructure exist before model needs arise.
• Mentor senior and mid-level engineers, participate in hiring, and grow team technical depth.
• Translate technical trade-offs into business terms for product and executive stakeholders, with documentation that preserves rationale over time.
• Continuously question assumptions, explore innovations not yet pursued, and improve business performance through persistent analysis.

Qualifications

• MS/PhD in a quantitative discipline (Computer Science, Math, Physics, Engineering, Statistics or other technical field) or equivalent experience.
• 4-8+ years building and shipping machine learning systems.
• For staff level: 3+ years operating at 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.
• Experience with language understanding over imperfect grammar (real-world search queries, menu and catalog text) is a strong plus.
• Strong data engineering fundamentals: PySpark, Hive/SQL, the Python data stack, and feature pipelines you can debug and build.
• Experimentation fluency: designing A/B tests, selecting guardrails, and recognizing when offline metrics can mislead.
• Experience with cloud ML infrastructure such as AWS/SageMaker, plus model deployment and production monitoring/observability.
• Demonstrated technical leadership through mentoring, driving architecture reviews beyond your team, and influencing decisions without direct authority.
• Ability to communicate performance metrics, model behavior, and technical trade-offs to technical and non-technical audiences, including executives.
• Comfort staying current with research publications and synthesizing them into production systems.
• Strong interest in self-motivated continuous learning.

Technologies

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

Compensation and location

• Location: New York, NY (onsite)
• Base salary range: USD 176,000 - 260,000 per year
• Senior MLE (New York Base Salary): $176,000 - $191,000
• Staff MLE (New York Base Salary): $208,500 - $225,000
• Senior Staff MLE (New York Base Salary): $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

Final note: Wonder participates in the federal government’s E-Verify program to confirm employment eligibility. If you need an accommodation during the interview process, contact your recruiter. Interviews will be scheduled and information shared via email or text.