Staff ML Platform Engineer - Java & Production ML

Wonder Group, INC; New York (NY); Hybrid; USD 209,000 - 217,000; Full time

Benefits offered by this job

Equity

401(k)

Medical, dental, vision plans

Disability coverage

Paid time off

Parental leave

Employee discounts

Job summary

Wonder Group seeks a Staff Machine Learning Engineer to provide hands‑on technical leadership across Java services and ML model integrations, from prototype to production. You will define standard architectures, drive performance improvements, and mentor engineers to build scalable ML platforms.

The role focuses on combining Java services with ML pipelines, ensuring reliability, latency and cost targets, and leading cross‑team architectural decisions in a hybrid work setting in New York.

Qualifications

• 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 operating high‑traffic backend services in Java.
• 5+ years building and operating ML systems in production, including 3+ years in a senior scope setting technical direction across teams or systems.
• Expertise in Java and object‑oriented design, including Spring Boot, REST or gRPC APIs, and JVM performance tuning.
• Proven experience setting load testing and performance standards using Gatling or JMeter, plus capacity planning.

Responsibilities

• Define the standard architecture for how Java services call ML models, including client libraries, API contracts, latency budgets, and fallback behavior.
• Set load testing, performance benchmarking, and capacity planning standards for every service that uses the models, and integrate those checks into the release process.
• Drive performance improvements through profiling, JVM tuning, caching, request batching, and concurrency work for high‑traffic model integrations.
• Lead the design and delivery of complex ML solutions across multiple teams or systems, making architecture decisions that span team boundaries.
• Build reusable ML architectures, pipelines, and deployment patterns to help Applied Science move work into production faster.
• Establish standards for ML reliability, scalability, monitoring, and lifecycle management, including model scorecards, reproducible training, safe rollouts, 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, and challenge technical choices when evidence supports it.
• Own the platform technical roadmap with engineering leadership by sequencing work across teams and surfacing risks early.
• Mentor senior and mid‑level engineers, participate in hiring, and build team depth so critical systems do not depend on one person.
• Translate technical trade‑offs into business terms for product and executive stakeholders, and document key decisions and rationale.

Skills

Java backend

ML systems

Distributed systems

Gatling/JMeter

Cloud (AWS/GCP)

Kubernetes

Python

Architecture leadership

Education

BS/MS/PhD in CS or related

Tools

Gatling

JMeter

Docker

Kubernetes

Job description

Wonder Group seeks a Staff Machine Learning Engineer to provide hands‑on technical leadership across Java services and ML model integrations, from prototype to production. You will define standard architectures, drive performance improvements, and mentor engineers to build scalable ML platforms.

The role focuses on combining Java services with ML pipelines, ensuring reliability, latency and cost targets, and leading cross‑team architectural decisions in a hybrid work setting in New York.