Staff Machine Learning Engineer

Wonder•New York, NY

20d•$208,500 - $216,500•Hybrid

About The Position

This is a hands-on technical leadership role, not a management role. The Staff Machine Learning Engineer owns model architecture across search ranking, homepage and topic recommendations, retrieval, and query understanding, and is accountable for how those areas work together. The role sets technical direction with staff engineers, product managers, and platform partners, improves how the organization builds, evaluates, and operates models, and mentors engineers through design reviews, code reviews, and direct feedback. The team owns projects end to end and focuses on personalized recommendation, retrieval, and classification using catalog content and clickstream data. Relevant approaches include deep neural networks, learned embeddings, approximate nearest-neighbor retrieval, transfer learning, calibration, regression, fine-tuning, and large language models. The role owns the ranking and recommendation stack, including candidate retrieval, multi-objective ranking, calibration, and the ensemble balancing conversion and profitability. It leads work to shift optimization toward long-term diner value, bringing research in information retrieval and recommender systems into production, and raising engineering and operational standards across teams. It partners with Product, Search Engineering, Ads, and Data Platform on roadmaps, data, and infrastructure; mentors and helps hire engineers; communicates technical trade-offs to business and executive stakeholders; and continually evaluates opportunities to improve business performance. The position follows a hybrid model requiring three days per week in the office, with employees welcome and encouraged to work onsite up to five days per week. New York base salary is $208,500–$216,500; offers may vary 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 deploying models in latency-sensitive applications.
Strong data engineering fundamentals, including PySpark, Hive/SQL, the Python data stack, and the ability to build and debug feature pipelines.
Fluency in experimentation: 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 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 follow current research publications and synthesize research into production systems.
Deep interest in self-motivated continuous learning.

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 the ensemble balancing conversion and profitability.
Make cross-system design decisions that span multiple model owners and technical areas.
Lead the shift in optimization objectives from short-term conversion toward long-term diner value, including offline evaluation and online experimentation.
Evaluate and bring relevant information retrieval and recommender-system 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 proactive technical-debt management.
Partner with Product, Search Engineering, Ads, and Data Platform to shape roadmaps, surface risks, and ensure required data and infrastructure are available.
Mentor senior- and mid-level engineers, participate in hiring, and grow the team's technical depth.
Explain technical trade-offs in business terms to product and executive stakeholders and document decision rationales.
Challenge existing assumptions, identify innovation opportunities, and analyze and improve business performance.