Staff Machine Learning Engineer, Model Platform

Wonder Group, INC is building a Model Platform where Java services reliably integrate with machine learning models at production scale. In this Staff Machine Learning Engineer role, you will provide hands-on technical leadership across architecture, performance, reliability, and cross-team platform decisions from prototype through rollout. The team’s focus is on making ML integrations fast, observable, and safe for tier-1 services.

Role Focus

As a Staff Machine Learning Engineer on the Model Platform team, you will define how Java services call ML models and help set the technical direction for performance, reliability, and cross-team ML architecture. You will also mentor engineers, lead reviews, and translate technical trade-offs for product and executive stakeholders.

Responsibilities

• Define the standard architecture for how Java services call ML models, including client libraries, API contracts, latency budgets, and fallbacks.
• Set standards for load testing, performance benchmarking, and capacity planning for every service calling models, and integrate them into the release process.
• Drive performance efforts such as profiling, JVM tuning, caching, request batching, and concurrency for high-traffic integrations.
• Lead the design and delivery of complex ML solutions spanning multiple teams or systems, making architecture decisions across team boundaries.
• Build reusable ML architectures, pipelines, and deployment patterns that accelerate Applied Science work to production.
• Set 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 the most demanding deployments, balancing accuracy against latency, scalability, and cost.
• Run design and code reviews across ML and software engineering teams, pushing back when 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, participate in hiring, and build team depth so critical systems do not depend on one person.
• Explain technical trade-offs in business terms to product and executive stakeholders, and document key decisions and their rationale.

Requirements

• 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 such as Spring Boot (or similar), REST or gRPC APIs, and JVM performance tuning.
• Experience setting load testing and performance standards (Gatling, JMeter, or similar) and planning capacity as load changes.
• Experience designing highly available distributed systems (99.99% uptime) with automated testing and monitoring built in from the start.
• Deep production ML architecture expertise, including training pipelines, batch and real-time serving, feature pipelines, model versioning, and rollback at scale.
• Strong Python and hands-on model development 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 and guardrail metrics, and understanding when offline metrics can be misleading.
• Proven technical leadership across ML and software engineering teams, including leading architecture reviews beyond your 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.

Technologies

• Java, Spring Boot, REST, gRPC, JVM performance tuning
• Gatling, JMeter
• Python, XGBoost, LightGBM, PyTorch, TensorFlow
• Spark, SQL
• AWS, GCP, SageMaker, Vertex AI
• Docker, Kubernetes, ONNX Runtime, TensorFlow Java, XGBoost4J
• Cassandra, Kafka, Flink

Location and Work Model

• Location: New York, NY (hybrid)
• Office requirement: Hybrid model requires 3 days per week in the office.
• Candidates are welcome and encouraged to be in the office up to 5 days per week if it works for them.

Compensation

• Salary (New York): USD 208,500 - 216,500 per year
• Wonder uses geographic-specific salary structures, and the final offer considers skills, education, training, credentials, and experience.

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
• Paid parental leave
• Discounted meals and exclusive perks across the Wonder family of brands

Additional Eligibility Note

Wonder does not sponsor applicants for employment visa status for this role.