Staff Machine Learning Engineer

Wonder Group • New York, NY

6d • Hybrid

About The Position

This is a hands-on technical leadership role, not a management position. The Staff Machine Learning Engineer owns model architecture across search ranking, homepage and topic recommendations, retrieval, and query understanding, ensuring that the systems work together. The role sets technical direction with staff engineers, product managers, and platform partners; raises standards for building, evaluating, and operating models; and mentors engineers through design reviews, code reviews, and direct feedback. The team focuses on personalized recommendation, retrieval, and classification using catalog content and clickstream data, with approaches including deep neural networks, learned embeddings, approximate nearest neighbor retrieval, transfer learning, calibration, regression, fine-tuning, and large language models.

The engineer owns the end-to-end ranking and recommendation architecture, including candidate retrieval, multi-objective ranking, calibration, and the ensemble balancing conversion and profitability. The role leads the shift toward optimizing long-term diner value, bringing research in information retrieval and recommender systems into production, and strengthening evaluation, training, deployment, reliability, and observability practices across teams. It partners with Product, Search Engineering, Ads, and Data Platform, mentors engineers, participates in hiring, and communicates technical trade-offs and decisions to business and executive stakeholders.

The role follows a hybrid model requiring three days per week in the office; employees may choose to work in the office up to five days per week. The New York base salary is $208,500–$216,500, with actual offers varying by location and candidate qualifications.

Requirements

• MS or PhD in a quantitative discipline such as Computer Science, Math, Physics, Engineering, or Statistics, or equivalent experience.
• At least 6 years building and shipping machine learning systems, including at least 3 years operating at senior-level scope and setting technical direction across multiple teams, model families, or systems.
• Deep production experience in recommendation systems, ranking, or information retrieval at scale, under real latency and cost constraints.
• Proven 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 deployment in latency-sensitive applications.
• Strong data engineering fundamentals, including PySpark, Hive/SQL, the Python data stack, and feature pipelines.
• Fluency in experimentation, including designing A/B tests, selecting guardrails, and recognizing when offline metrics are misleading.
• Experience with cloud ML infrastructure such as AWS/SageMaker or equivalent, model deployment, and production monitoring and observability.
• Demonstrated technical leadership, including mentoring engineers, driving design and architecture reviews beyond one's own team, and influencing without direct authority.
• Ability to communicate performance metrics, model behavior, and technical trade-offs to technical and non-technical audiences, including executives.
• Ability to keep up with research publications and synthesize research into production systems.
• Strong interest in self-motivated continuous learning.
• Ability to work in the hybrid model, which requires three days per week in the office.

Nice To Haves

• Experience with language understanding over imperfect grammar, such as real-world search queries and menu or catalog text.

Responsibilities

• Own the end-to-end architecture of the ranking and recommendation stack, including candidate retrieval, multi-objective ranking, calibration, and an ensemble balancing conversion and profitability.
• Make cross-system design decisions across model families and systems.
• Lead the evolution of the optimization objective from short-term conversion toward long-term diner value, including offline evaluation and online experimentation.
• Evaluate and bring applicable information retrieval and recommender systems research into production, including LLM-driven query and intent understanding, embedding-based retrieval, sequential user representations for cold start, and real-time inference.
• Improve engineering and operational standards across teams, including model evaluation and scorecards, reproducible training pipelines, safe deployment, SLOs, observability for tier-1 models, and technical debt management.
• Partner with Product, Search Engineering, Ads, and Data Platform to shape roadmaps, surface risks, and ensure necessary data and infrastructure are available.
• Mentor senior and mid-level engineers, participate in hiring, and strengthen team technical depth to reduce dependence on individual contributors for critical systems.
• Translate technical trade-offs into business terms for product and executive stakeholders and document decision rationale.
• Challenge existing assumptions and analyze and improve business performance.

Benefits

• Competitive compensation package with equity and a 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, and paid sick leave in compliance with applicable law.
• Paid parental leave.
• Discounted meals and exclusive perks across the Wonder family of brands.

What This Job Offers

• Job Type: Full-time
• Career Level: Senior