Staff Machine Learning Engineer

Wonder Group, INC; United States; Hybrid; USD 209,000 - 217,000; Full time

Benefits offered by this job

Equity

401(k)

Medical plans

Paid time off

Parental leave

Discounted meals

Job summary

Wonder Group, INC seeks a hands-on technical leader to own the architecture of its ranking and recommendation stack, spanning candidate retrieval, multi-objective ranking, and calibration. You will push for state-of-the-art research, implement end-to-end evaluation, and raise standards across teams with robust pipelines and observability.

You will mentor engineers, work with product, search, ads, and data platform teams, and translate complex trade-offs into actionable business decisions, while

Qualifications

• 6+ years building and shipping ML systems, including 3+ years at senior scope.
• Deep experience in recommendation systems, ranking, or information retrieval at scale.
• Proven track record with production deep learning in TensorFlow or PyTorch.
• Experience with Large Language Models and transformer architectures.
• Strong data engineering fundamentals: PySpark, Hive/SQL, Python data stack.
• Fluency with experimentation: designing A/B tests and guardrails.
• Experience with cloud ML infrastructure and production monitoring.
• Demonstrated technical leadership: mentoring engineers and driving reviews.
• Ability to communicate metrics and trade-offs to technical and executive audiences.
• Continuous learning and keeping up with research publications.

Responsibilities

• Own the architecture of ranking and recommendation stack end to end.
• Lead cross-system design decisions and alignment with product and data platform.
• Drive offline evaluation and online experimentation strategies.
• Bring state-of-the-art IR and recommender research into production.
• Ensure scalable, observable model deployment and SLOs across teams.
• Mentor engineers and participate in hiring to grow technical depth.

Skills

Deep learning

TensorFlow

PyTorch

LLMs

Information retrieval

A/B testing

Technical leadership

Education

MS/PhD in quantitative discipline

Tools

AWS SageMaker

Job description

About The Opportunity

This is a hands-on technical leadership role, not a management role. You will own model architecture across several connected technical areas: search ranking, homepage and topic recommendations, retrieval, and query understanding. You will be accountable for how those pieces fit together, not only for any single model. You will set technical direction alongside other staff engineers, product managers, and platform partners. You will raise the bar on how the organization builds, evaluates, and operates models, and mentor the engineers around you through design reviews, code reviews, and direct feedback. We expect you to challenge technical decisions across teams when the engineering case is clear, and to bring evidence when you do. Our team practices end to end project ownership, and our work focuses heavily on personalized recommendation, retrieval, and classification from catalog content and clickstream. Deep neural networks, learned embeddings and approximate nearest neighbor retrieval, transfer learning from pre-trained large scale models, calibration, classic regressions, fine tuning, and large language models all have a place in our daily lexicon.

The Impact You Will Make Own the architecture of our ranking and recommendation stack end to end: candidate retrieval, multi-objective ranking, calibration, and the ensemble that trades conversion against profitability. Make the cross-system design calls that no individual model owner can make alone. Lead the evolution of our optimization objective from short-term conversion toward long-term diner value, including the offline evaluation and online experimentation work required to trust the result before it ships. Bring state of the art research in information retrieval and recommender systems into our runtime environment: LLM-driven query and intent understanding, embedding-based retrieval, sequential user representations for cold start, and real-time inference. Assess rigorously what actually transfers to our traffic, and say no to what does not. Raise engineering and operational standards across multiple teams: model evaluation and scorecards, reproducible training pipelines, safe deployment, SLOs and observability for tier-1 models, and proactive management of technical debt before it becomes urgent. Partner with Product, Search Engineering, Ads, and Data Platform to shape roadmaps, surface risk early, and make sure the data and infrastructure exist before the model needs them. Mentor senior and mid-level engineers, participate in hiring, and grow the technical depth of the team so that no critical system depends on a single person. Translate technical trade-offs into business terms for product and executive stakeholders, and document the rationale clearly enough that decisions outlive the people who made them. Question existing assumptions, look for the innovation we are not yet pursuing, and relentlessly analyze and improve the performance of our business.

What You Bring to the Table

• MS/PhD in a quantitative discipline (Computer Science, Math, Physics, Engineering, Statistics or other technical field) or equivalent experience
• 6+ years building and shipping machine learning systems, including 3+ years operating at senior-level scope: setting technical direction across multiple teams, model families, or systems
• Deep experience in recommendation systems, ranking, or information retrieval at scale, in production and under real latency and cost constraints
• Proven track record with production deep learning in 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 them 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 as well as build
• Fluency with experimentation: designing A/B tests, choosing the right guardrails, and recognizing when an offline metric is misleading you
• Experience with cloud ML infrastructure (AWS/SageMaker or equivalent), model deployment, and production monitoring and observability
• Demonstrated technical leadership: mentoring engineers, driving design and architecture reviews beyond your own team, and influencing decisions without direct authority
• Comfort communicating performance metrics, model behavior, and technical trade-offs to both deeply technical and non-technical audiences, up to and including executive stakeholders
• Ability to keep up with the latest research publications and synthesize them into working production systems
• Deep interest in self-motivated continuous learning

Our hybrid model requires 3 days a week in the office. That said, many team members choose to come in more often to take advantage of in-person collaboration and connection. You're welcome—and encouraged—to be in the office up to 5 days a week if it works for you.

New York Base Salary: $208,500 - $216,500. Wonder uses geographic-specific salary structures, which means the salary offered may vary depending on where the job is located. The final salary offer will take into account various factors, such as the candidate's skills, education, training, credentials, and experience.

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
• paid sick leave in compliance with applicable law in addition to paid parental leave
• discounted meals
• exclusive perks across the Wonder family of brands

Eligibility, effective dates, and available plan options vary by employment classification and location. To learn more about benefits for this role, visit our Careers page here.

A Final Note

At Wonder, we build the best teams by hiring with an objective lens—evaluating people for their potential while championing diversity, equity, and inclusion. We do not discriminate based on race, color, religion, gender identity or expression, sexual orientation, national origin, age, military service eligibility, veteran status, marital status, disability, or any other protected class. As part of our commitment to fair and compliant hiring practices, Wonder participates in the federal government's E-Verify program to confirm employment eligibility. If you need an accommodation during the interview process, please let your recruiter know.

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There has never been a more exciting time to join our world-class team. We've brought together people who are changing the way the world eats—with grit, optimism, care, and a little bit of salt, and pepper. And we're just getting started.