Lead Software Engineer

engineeringjobs.net, Inc.; New York (NY); On-site; USD 140,000 - 200,000; Full time

Benefits offered by this job

Competitive base salary

Total rewards package

Discretionary incentive compensation

Equity eligibility

Comprehensive healthcare

Wellness benefits

Professional development

Career growth opportunities

Job summary

engineeringjobs.net, Inc. seeks an engineer to build and maintain robust pipelines for distributed training on GPU-enabled clusters and to manage the model development life cycle.

You will collaborate with cross-functional teams to integrate new technologies and optimize training throughput for large data sources.

Qualifications

• Experience building data pipelines for ML workloads.
• Hands-on with Python, cloud platforms and container orchestration.
• Familiarity with PyTorch or TensorFlow and big data tools.

Responsibilities

• Build and maintain robust pipelines for distributed training on GPU-enabled clusters.
• Manage the model development life cycle and collaborate with cross-functional teams to integrate new technologies.

Skills

Python

Cloud computing

PyTorch

TensorFlow

Data science

Model deployment

Monitoring

Observability

Big data

Ray

Spark

vLLM

Enterprise system design

Kubernetes

Distributed training

GPU clusters

Job description

You will build and maintain robust pipelines for distributed training on GPU-enabled clusters and manage the model development life cycle. Additionally, you will collaborate with cross-functional teams to integrate new technologies and optimize training throughput for large data sources.

Requirements

The role requires extensive experience in Python, cloud computing, and deep learning frameworks like PyTorch or TensorFlow. Candidates must possess a solid grounding in engineering fundamentals, big data tools, and container orchestration systems such as Kubernetes.

Key Skills

• Python
• Cloud computing
• PyTorch
• TensorFlow
• Data science
• Model deployment
• Monitoring
• Observability
• Big data
• Ray
• Spark
• vLLM
• Enterprise system design
• Kubernetes
• Distributed training
• GPU clusters

Benefits

• Competitive base salary
• Total rewards package
• Discretionary incentive compensation
• Equity eligibility
• Comprehensive healthcare
• Wellness benefits
• Professional development
• Career growth opportunities