The Judge Group
Position Summary
The Machine Learning Engineer will design, implement, and optimize production‑ready ML models, working closely with data scientists and engineers to deliver innovative AI solutions. The role ensures models are performant, maintainable, and seamlessly integrated with enterprise systems, reporting to the Vice President, Technology Innovation.
Day-to-Day Responsibilities
Build and deploy machine learning models into production environments
Optimize model training and inference pipelines for performance and scalability
Collaborate with data engineers to design robust feature pipelines
Leverage APIs and microservices for integrating AI models into enterprise applications
Ensure models are explainable, reliable, and compliant with regulatory requirements
Work with cloud-native ML services (Azure ML, Kubernetes, Docker)
Research and implement state-of-the-art ML methods for business applications
Apply NVIDIA NIMs for model optimization and scaling, and use NeMo services for model development and fine-tuning
Qualifications
10+ years of experience developing data-related solutions and software
5+ years as an ML Engineer or Software Engineer with ML focus
5+ years of proficient experience with Python and ML frameworks (TensorFlow, PyTorch, Scikit-learn)
Experience with distributed training and optimization on GPUs (CUDA, RAPIDS)
Familiarity with data pipelines (Spark, Databricks, Kafka)
Hands‑on experience with CI/CD for ML workflows and container orchestration (Docker, Kubernetes)
Strong knowledge of algorithms, data structures, and ML system design
Practical experience deploying on Azure AI services, and using NVIDIA NeMo and NIMs for LLM and generative AI workloads
Bachelor’s degree in computer science, Machine Learning, or related field
Benefits
Medical insurance
Vision insurance
401(k)
Paid maternity leave
Paid paternity leave
Pension planTuition assistance
Employment Type Full‑time
Seniority Level Mid‑Senior level
Location Camas, WA
#J-18808-Ljbffr
Day-to-Day Responsibilities
Build and deploy machine learning models into production environments
Optimize model training and inference pipelines for performance and scalability
Collaborate with data engineers to design robust feature pipelines
Leverage APIs and microservices for integrating AI models into enterprise applications
Ensure models are explainable, reliable, and compliant with regulatory requirements
Work with cloud-native ML services (Azure ML, Kubernetes, Docker)
Research and implement state-of-the-art ML methods for business applications
Apply NVIDIA NIMs for model optimization and scaling, and use NeMo services for model development and fine-tuning
Qualifications
10+ years of experience developing data-related solutions and software
5+ years as an ML Engineer or Software Engineer with ML focus
5+ years of proficient experience with Python and ML frameworks (TensorFlow, PyTorch, Scikit-learn)
Experience with distributed training and optimization on GPUs (CUDA, RAPIDS)
Familiarity with data pipelines (Spark, Databricks, Kafka)
Hands‑on experience with CI/CD for ML workflows and container orchestration (Docker, Kubernetes)
Strong knowledge of algorithms, data structures, and ML system design
Practical experience deploying on Azure AI services, and using NVIDIA NeMo and NIMs for LLM and generative AI workloads
Bachelor’s degree in computer science, Machine Learning, or related field
Benefits
Medical insurance
Vision insurance
401(k)
Paid maternity leave
Paid paternity leave
Pension planTuition assistance
Employment Type Full‑time
Seniority Level Mid‑Senior level
Location Camas, WA
#J-18808-Ljbffr