Model Drift Detection Engineer Jobs

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Today

Staff Machine Learning Engineer

Xometry Lexington, KY

Lexington, KY

Today

Full time Ai Ml Artificial Intelligence Cloud

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Job Description

Lead the design and delivery of mission-critical ML systems for Xometry’s AI/ML integration, with emphasis on real-time serving and low-latency data flows into partner tooling.

Responsibilities

Own end-to-end delivery from requirements gathering through release, ensuring high-quality, on-time outcomes across complex, cross-functional initiatives
Architect and build partner integration ML, delivering a high-performance AI/ML layer for the embedded DFM AI + IQE integration with Teamcenter and Designcenter
Design real-time ML serving architecture and a low-latency signal path that returns DFM and pricing feedback directly into the designer environment
Define data contracts for model inputs and outputs; implement MLOps, governance, and observability for a mission-critical public-marketplace partner integration
Develop cloud-based production systems including real-time endpoints and MLOps integrated with Xometry’s broader systems and infrastructure
Handle cross-domain technical challenges by evaluating variable factors and aligning solutions with both business and technical objectives
Surface opportunity areas, drive new processes and solutions, and build multi-quarter roadmaps for key technical goals
Apply best practices across automated testing, parallel and distributed computing, and secure software development for ML systems
Collaborate with engineers, product managers, data scientists, and business stakeholders to translate requirements into robust technical solutions
Conduct design reviews, code reviews, and provide technical mentorship to raise team capability
Stay current with ML/AI advances and introduce relevant new approaches, tools, and frameworks

Requirements

Bachelor’s degree in a STEM field (or equivalent experience) plus 6-8 years of experience in machine learning engineering, with proven ownership of complex production ML systems
Strong expertise in ML and AI technologies, including Gradient Boosting, Deep Learning, and/or Generative AI frameworks, with emphasis on backend scalability and reusability
Hands-on experience deploying real-time ML products at scale in cloud environments (with AWS strongly preferred), including auto-scaling, monitoring, and alerting
Advanced proficiency in Python and ML/AI frameworks such as TensorFlow or PyTorch
Solid software engineering fundamentals, including data structures and algorithms
Experience with MLOps: model monitoring, data and concept drift detection, and automated retraining plus redeployment pipelines
Proficiency with CI/CD pipelines (e.g., GitHub Actions), test-driven development, and infrastructure as code (e.g., Terraform)
Demonstrated ability to profile and optimize existing ML deployments for latency and throughput
Capability to operate independently on new and ambiguous assignments, select methods and procedures, and communicate effectively across engineering, product, and business audiences
Experience with modern modeling approaches including transformers, self-supervised pre-training, large language models (LLMs), or generative AI
Knowledge of containers, Kubernetes, and cloud-native distributed systems
Manufacturing, supply chain, or marketplace background is a plus, with emphasis that curiosity and drive matter

Technologies

Python
TensorFlow
PyTorch
Gradient Boosting
Deep Learning
Generative AI frameworks
AWS
CI/CD pipelines
GitHub Actions
Test-driven development
Terraform
MLOps
Model monitoring
Data and concept drift detection
Auto-scaling
Containers
Kubernetes
Transformers
Self-supervised pre-training
Large language models (LLMs)
Solid Edge
NX
Designcenter
Teamcenter
Parallel and distributed computing

Benefits

401(k) match
Medical, dental and vision insurance
Life and disability insurance
Generous paid time off including vacation, sick leave, floating and fixed holidays
Maternity and bonding leave
EAP and other wellbeing resources

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