The posting
About Wonder
Everything’s on the menu at Wonder. Except compromise.
Wonder is the mealtime platform built to feed every craving in one order. With Wonder, you can mix and match hundreds of dishes from 20+ exclusive restaurants in a single order, so everyone gets exactly what they’re craving. Enjoy everything from tacos to Thai with $0 delivery fees, plus dine-in or pick up at a Wonder location near you. Each dish is made to order on-site by our culinary team and served hot.
Behind the scenes, Wonder owns and operates every step of the dining experience, from recipe development to the technology that powers our experience, allowing us to deliver quality, variety, and consistency at scale.
Join us as we work to make great food more accessible.
About The Opportunity Wonder's Model Platform team builds the systems that take machine learning models from prototype to production and keeps them running well once they're there. Our models rank restaurants and dishes, predict delivery times, price orders, and dispatch couriers for millions of diners across Grubhub and Wonder. Most of those predictions are served to high-traffic Java services. How well a model works with those services matters as much as how good the model is.
We're hiring a Staff Machine Learning Engineer with deep Java backend experience to set the technical direction for how our models and services work together. You'll define how every team calls our models. You'll set the standards for load testing and performance across the services that use them. You'll also lead complex ML work that spans multiple teams and systems.
This is a hands-on technical leadership role. You'll write code, design models, and take on the hardest production problems yourself. You'll be the main technical link between Model Platform and our software engineering teams, and you'll partner with Applied Scientists, our data and infrastructure platform teams, and senior engineering leadership. Our interview process includes the same Java coding interview our software engineers take.
The Impact You Will Make
- Define the standard architecture for how Java services call ML models, including client libraries, API contracts, latency budgets, and fallbacks.
- Set the standards for load testing, performance benchmarking, and capacity planning for every service that calls our models, and build them into the release process.
- Drive performance work such as profiling, JVM tuning, caching, request batching, and concurrency for our highest-traffic model integrations.
- Lead the design and delivery of complex ML solutions that span multiple teams or systems, and make the architecture decisions that cross team boundaries.
- Build reusable ML architectures, pipelines, and deployment patterns that get Applied Science work into production faster.
- Set the standards for ML reliability, scalability, monitoring, and lifecycle management, including model scorecards, reproducible training, safe rollout, and SLOs for tier-1 models.
- Own model architecture and tuning for our most demanding deployments, balancing accuracy against latency, scalability, and cost.
- Run design and code reviews across ML and software engineering teams, and push back on technical decisions when the evidence supports it.
- Own the platform's technical roadmap with engineering leadership, sequence work across teams, and raise risks early.
- Mentor senior and mid-level engineers, take part in hiring, and build depth on the team so no critical system depends on one person.
- Explain technical trade-offs in business terms to product and executive stakeholders, and write down key decisions and the reasons behind them.
What You Bring to the Table
- BS, MS, or PhD in Computer Science, Engineering, Mathematics, Statistics, or a related field, or equivalent experience
- 8+ years of professional software engineering experience, with deep expertise building and running high-traffic backend services in Java
- 5+ years building and running ML systems in production, including 3+ years at senior scope setting technical direction across teams or systems
- Expert Java and object-oriented design skills, including service frameworks (Spring Boot or similar), REST or gRPC APIs, and JVM performance tuning
- A track record of setting load testing and performance standards (Gatling, JMeter, or similar) and planning capacity for services as load changes
- Experience designing highly available distributed systems (99.99% uptime) with automated testing and monitoring built in from the start
- Deep expertise in production ML architecture, including training pipelines, batch and real-time serving, feature pipelines, model versioning, and rollback at scale
- Strong Python and hands-on model development experience with common frameworks (XGBoost/LightGBM, PyTorch, or TensorFlow), including choosing and tuning models under real latency and cost limits
- Strong data engineering skills in Spark and SQL, including building and debugging streaming or batch pipelines
- Deep experience with cloud infrastructure on AWS or GCP (SageMaker, Vertex AI, or equivalent), Docker, and Kubernetes
- Experience with experimentation, including A/B test design, guardrail metrics, and knowing when an offline metric is misleading
- Proven technical leadership across ML and software engineering teams, including leading architecture reviews beyond your own team, influencing decisions without direct authority, and mentoring senior engineers
- Clear communication of model behavior and technical trade-offs to technical and non-technical audiences, including executives
Got These? Even Better
- Experience in marketplace, e-commerce, or logistics, such as ranking, recommendations, ETA prediction, pricing, or dispatch
- Serving models inside or alongside JVM services (ONNX Runtime, TensorFlow Java, XGBoost4J, or similar)
- Building an internal ML platform used by multiple science teams
- Cassandra or other distributed NoSQL databases
- Streaming data systems (Kafka, Flink)
- Leading an ML platform migration between cloud
As a matter of company policy, Wonder does not sponsor applicants for employment visa status for this role.
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: $208,500 - $216,500 per year
#LI-Hybrid
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
The benefits applicable to this role include a competitive compensation package with equity and a 401(k). We also offer a 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 in addition to paid parental leave, discounted meals and 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.
We look forward to hearing from you! We'll contact you via email or text to schedule interviews and share information about your candidacy.
