Staff Machine Learning Engineer – ML Frameworks

Jobtailor; California (MO); On-site; USD 150,000 - 210,000; Full time

Job summary

Jobtailor seeks an experienced AI/ML infrastructure engineer to design, implement, and scale training workflows on AWS using Kubernetes and Python. You will optimize distributed training, improve model serving, and enhance resource utilization for large-scale models.

Collaborate with data scientists to streamline pipelines, contribute to AutoML tools, and advance orchestration. Ideal candidates hold a PhD or MS in CS with 5+ years of hands-on experience and strong communication and teamwork.

Qualifications

• PhD or Master’s in computer science or related field and 5+ years of hands-on industry experience.
• Proven proficiency with Python and developing systems, frameworks and SDKs.
• Experience with infrastructure and understanding of model serving, training, orchestration, and management of GPU resources.
• Experience with machine learning and distributed PyTorch.
• Strong critical thinking, analytical and quantitative problem-solving ability.
• Excellent communication, relationship skills and a strong teammate.
• Experience with KubeFlow, MLFlow, Ray, SageMaker, or similar (added plus).
• Experience with PyTorch distributed, MPI, Megatron, Horovod and other AI training frameworks (added plus).

Responsibilities

• Design, develop, and maintain robust AI/ML infrastructure solutions.
• Improve distributed training frameworks and scalability on GPUs.
• Enhance resiliency, elasticity, data loading, and model parallelism support.
• Improve orchestration and scheduling for faster model training.
• Scale jobs and enable faster experimentation with AutoML tools.
• Collaborate with data scientists to streamline training pipelines and optimize resource utilization.
• Drive innovation in infrastructure practices supporting ML research and development.

Skills

Python Proficiency

Kubernetes Experience

Distributed PyTorch Knowledge

AI/ML Infrastructure Development

GPU Resource Management

Education

PhD in Computer Science

Master’s in Computer Science

Tools

KubeFlow

MLFlow

Ray

SageMaker

PyTorch Distributed

MPI

Megatron

Horovod

Job description

• Design, develop, and maintain robust AI/ML infrastructure solutions supporting training and deployment of large-scale AI models using Kubernetes and Python on AWS cloud
• Implement and improve distributed training frameworks leveraging GPUs to improve performance and scalability
• Improve resiliency, elasticity, data loading, and out-of-the-box support for FSDP and model parallelism
• Improve orchestration and scheduling to train better models
• Scale the number of jobs and enable faster experimentation with AutoML and similar tools
• Collaborate with data scientists and ML researchers to streamline model training pipelines and ensure efficient resource utilization
• Drive innovation in infrastructure practices supporting machine learning research and development

Requirements

• PhD or Master’s in computer science or related field and 5+ years of hands-on industry experience
• Proven proficiency with Python and developing systems, frameworks and SDKs
• Experience with infrastructure and understanding of model serving, training, orchestration, and management of GPU resources
• Experience with machine learning and distributed PyTorch
• Strong critical thinking, analytical and quantitative problem-solving ability
• Excellent communication, relationship skills and a strong teammate
• Experience with KubeFlow, MLFlow, Ray, SageMaker, or similar (added plus)
• Experience with PyTorch distributed, MPI, Megatron, Horovod and other AI training frameworks (added plus)

Core Competencies

Demonstrates expertise in designing and maintaining AI/ML infrastructure solutions, with a strong focus on Python, Kubernetes, and AWS. Proven ability to enhance distributed training frameworks and optimize resource utilization for large-scale AI model deployment.

Highest-signal resume keywords

• Python Proficiency
• Kubernetes Experience
• Distributed PyTorch Knowledge
• AI/ML Infrastructure Development
• GPU Resource Management

ATS Optimization Keywords

Hard Skills

• AI/ML Infrastructure Solutions
• Distributed Training Frameworks
• Model Serving
• Orchestration and Scheduling
• AutoML Tools
• Python Development
• Critical Thinking
• Analytical Problem-Solving
• Quantitative Analysis
• Machine Learning

Soft Skills

• Excellent Communication
• Relationship Skills
• Team Collaboration

Certifications & Qualifications

• PhD in Computer Science
• Master’s in Computer Science

Industry Keywords

• AI Models
• Machine Learning Research
• Infrastructure Practices
• Resource Utilization
• Scalability

Tools & Technologies

• KubeFlow
• MLFlow
• Ray
• SageMaker
• PyTorch Distributed
• MPI
• Megatron
• Horovod