Machine Learning Engineer

Wonder; New York (NY); On-site; USD 180,000 - 260,000; Full time

Benefits offered by this job

Equity

401(k)

Medical, dental, vision plans

Disability coverage

Paid time off

Vacation

Sick leave

Job summary

Wonder is seeking a senior ML architect to own the end-to-end ranking and recommendation stack in New York. You will lead cross-system design, integrate LLM-driven understanding and embedding-based retrieval, and drive experiments to balance conversion with profitability.

You will mentor engineers and work with product and data teams to shape scalable, observable systems. You will be responsible for elevating engineering standards, building reproducible pipelines, and communicating complex

Qualifications

• MS/PhD in a quantitative discipline or equivalent experience.
• 4-8+ years building and shipping machine learning systems.
• For staff level and above: 3+ years operating at senior scope across multiple teams or systems.
• Deep experience in recommendation systems, ranking, or information retrieval at scale with latency and cost constraints.
• Production DL experience with TensorFlow or PyTorch, including training, serving, and tuning on GPUs.
• Experience with Large Language Models and transformer-based architectures, including fine-tuning and embedding generation.
• Strong data engineering fundamentals: PySpark, Hive/SQL, Python data stack.
• Experimentation fluency: designing AB tests and guardrails.

Responsibilities

• Lead the end-to-end architecture of the ranking and recommendation stack, including candidate retrieval, multi-objective ranking, calibration, and an ensemble balancing conversions and profitability.
• Make cross-system design decisions that individual model owners cannot complete alone.
• Drive evolution of optimization objectives from short-term conversions toward long-term diner value with offline evaluation and online experimentation.
• Integrate state-of-the-art research into runtime systems for information retrieval and recommender systems (LLM-driven query understanding, embedding-based retrieval, sequential user representations).
• Evaluate rigorously what transfers to production traffic and discard non-transferable approaches.
• Raise engineering and operational standards across teams (model evaluation, reproducible training pipelines, safe deployment, SLOs, observability, technical debt).
• Partner with Product, Search Engineering, Ads, and Data Platform to shape roadmaps and ensure data and infra exist before model needs are imminent.
• Mentor senior and mid-level engineers, participate in hiring, and grow technical depth so critical systems do not depend on a single person.
• Translate technical trade-offs into business terms for product and execs; document rationale clearly.
• Continuously improve business performance through deep analysis and exploring innovations.

Skills

Ranking systems

Information retrieval

Leadership

Mentoring engineers

Communication to executives

Self-directed learning

Education

MS/PhD in quantitative discipline

Tools

TensorFlow

PyTorch

GPUs

PySpark

Hive/SQL

Python

AWS/SageMaker

Job description

Own end-to-end machine learning architecture for search ranking and personalized recommendations in New York.

Responsibilities

• Lead the architecture of the ranking and recommendation stack end to end, including candidate retrieval, multi-objective ranking, calibration, and an ensemble that balances conversion against profitability
• Make cross-system design decisions that individual model owners cannot complete alone
• Drive the evolution of the optimization objective from short-term conversion toward long-term diner value, including required offline evaluation and online experimentation to build confidence before release
• Integrate state-of-the-art research into runtime systems 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
• Evaluate rigorously what transfers to production traffic and decline approaches that do not
• Raise engineering and operational standards across multiple teams, covering:
  • model evaluation and scorecards
  • reproducible training pipelines
  • safe deployment
  • SLOs and observability for tier-1 models
  • proactive technical debt management
• Partner with Product, Search Engineering, Ads, and the Data Platform to shape roadmaps, surface risk early, and ensure data and infrastructure exist before model needs are imminent
• Mentor senior and mid-level engineers, participate in hiring, and grow technical depth so critical systems do not depend on a single person
• Translate technical trade-offs into business terms for product and executive stakeholders and document the rationale clearly
• Question existing assumptions, identify innovations not yet pursued, and continuously improve business performance through deep analysis

Requirements

• 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 and above: 3+ years operating at senior scope, setting technical direction across multiple teams, model families, or systems
• Deep experience in recommendation systems, ranking, or information retrieval at scale with production constraints for latency and cost
• 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
• Language understanding over imperfect grammar (real-world search queries, menu and catalog text) is a strong plus
• Strong data engineering fundamentals: PySpark, Hive/SQL, Python data stack, and debuggable feature pipelines
• Experimentation fluency: designing A/B tests, selecting guardrails, and recognizing when offline metrics mislead
• Experience with cloud ML infrastructure (AWS/SageMaker or equivalent), model deployment, and production monitoring and observability
• Demonstrated technical leadership: mentoring engineers, 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
• Ability to keep up with the latest 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

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