Staff Machine Learning Engineer (Model Platform)

Quick Facts

• Role: Staff Machine Learning Engineer (Model Platform)

• Focus: ML model platform serving and production reliability for high-traffic Java services

Description

In this hands-on technical leadership role, you will define the architecture and standards for how Java services call machine learning models, including API contracts, latency budgets, and fallbacks. You will set load testing, performance benchmarking, and capacity planning processes, then drive JVM/service and ML performance work in production. You’ll also lead complex ML solutions across multiple teams and systems, building reusable pipelines and deployment patterns to get work into production faster.

Responsibilities

• Define standard architecture for Java services calling ML models (client libraries, API contracts, latency budgets, fallbacks)

• Establish load-testing, performance benchmarking, and capacity-planning standards and integrate them into the release process

• Drive profiling, JVM tuning, caching, request batching, and concurrency for high-traffic model integrations

• Lead cross-team ML solution design and delivery; make architectural decisions across boundaries

• Build reusable ML architectures, pipelines, and deployment patterns for faster productionization

• Set standards for ML reliability, scalability, monitoring, and lifecycle management (scorecards, reproducible training, safe rollout, SLOs)

• Own model architecture and tuning for demanding deployments, balancing accuracy, latency, scalability, and cost

• Run ML/software engineering design and code reviews; challenge decisions with evidence

• Own the platform technical roadmap; sequence work across teams and surface risks early

• Mentor engineers, participate in hiring, and build team depth

• Communicate technical trade-offs to product and executive stakeholders

Requirements

• 8+ years of professional software engineering experience building/running high-traffic backend services in Java

• 5+ years building/running ML systems in production, including 3+ years setting technical direction across teams/systems

• Strong Java and object-oriented design; service frameworks (Spring Boot or similar) and REST or gRPC APIs

• Experience setting load testing and performance standards (Gatling, JMeter, or similar) and planning capacity

• Experience designing highly available distributed systems with automated testing and monitoring

• Deep production ML architecture expertise: training pipelines, batch & real-time serving, feature pipelines, model versioning, rollback at scale

• Strong Python with hands-on model development using common frameworks (XGBoost/LightGBM, PyTorch, TensorFlow), with tuning under latency/cost constraints

• Strong data engineering skills with Spark and SQL for batch/streaming pipelines

• Cloud infrastructure experience on AWS or Google Cloud (SageMaker, Vertex AI, or equivalent), plus Docker and Kubernetes

• Experimentation experience (A/B tests, guardrail metrics) and understanding of offline-vs-online metric gaps

• Proven ML and software engineering technical leadership: architecture reviews beyond your team, influence without direct authority, and mentoring

Benefits

• Competitive compensation 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) and paid vacation

• Paid sick leave and paid parental leave

• Discounted meals and exclusive perks