Home Jobs Software Engineer - LLM Training
Back to Jobs

Software Engineer - LLM Training - Job Opportunity at CentML

San Francisco Bay Area, United States
Full-time
Senior
Posted: April 19, 2025
Hybrid
USD 180,000 - 250,000 per year plus equity, based on Bay Area market rates for senior ML infrastructure engineers

Benefits

Competitive equity package with early-stage stock options and growth potential
Comprehensive medical and dental coverage with best-in-class providers
Family-friendly parental leave with top-up benefits
Flexible vacation policy promoting work-life balance
Professional development investment program
Inclusive and diverse workplace culture

Key Responsibilities

Lead architectural design and implementation of distributed training systems for large-scale LLMs
Optimize cross-GPU parallelization strategies for enterprise-level model training
Develop core system components for maximizing computational efficiency
Transform research innovations into production-ready training systems
Drive technical collaboration between research and engineering teams
Design and implement scalable APIs and user interfaces
Conduct system-wide performance optimization and debugging

Requirements

Education

Bachelor's, Master's, or PhD's degree in Computer Science/Engineering, Software Engineering, related field or equivalent working experience

Experience

3+ years of software development experience

Required Skills

Python and C++ development Deep learning frameworks (PyTorch, Megatron Core, DeepSpeed) Distributed systems and parallel computing GPU programming (CUDA, NCCL) Performance optimization and profiling Cloud platforms (AWS, GCP, Azure) Docker and Kubernetes Machine learning fundamentals Problem-solving and debugging
Advertisement
Ad Space

Sauge AI Market Intelligence

Industry Trends

The LLM training infrastructure market is experiencing rapid growth with increasing demand for efficient distributed training solutions. Companies are investing heavily in optimizing training costs and infrastructure efficiency. There's a significant shift towards specialized LLM training platforms as organizations seek to reduce the computational costs and complexity of AI model development. The industry is seeing increased focus on sustainable AI practices, with emphasis on training efficiency and resource optimization.

Role Significance

Likely part of a core engineering team of 5-10 engineers, working closely with research scientists and product teams in a fast-paced startup environment
Senior individual contributor role with significant technical ownership and architectural decision-making authority. The position involves core platform development that directly impacts the company's main value proposition.

Key Projects

Development of distributed training infrastructure for large-scale language models Implementation of novel parallelization strategies for multi-GPU environments Creation of developer-friendly APIs for ML training optimization Performance optimization systems for enterprise-scale model training

Success Factors

Deep technical expertise in distributed systems and ML infrastructure Ability to bridge research innovations with production engineering requirements Strong system-level problem-solving skills and performance optimization experience Effective collaboration with cross-functional teams in a research-driven environment

Market Demand

Very high demand with limited talent pool, particularly for engineers with distributed training expertise. The role combines specialized ML systems knowledge with traditional software engineering, making qualified candidates highly sought after.

Important Skills

Critical Skills

Distributed systems expertise is crucial for designing scalable training architectures Deep understanding of ML infrastructure and training dynamics enables optimization of large-scale systems Performance optimization skills are essential for achieving cost-effective training solutions

Beneficial Skills

Frontend development skills for building user interfaces DevOps practices for maintaining robust infrastructure MLOps knowledge for end-to-end model lifecycle management

Unique Aspects

Direct work on cutting-edge LLM training optimization problems
Opportunity to shape core infrastructure in a growing AI company
Team led by recognized experts in ML systems
Focus on democratizing AI through cost reduction

Career Growth

2-3 years in role with potential for rapid advancement based on company growth and individual impact

Potential Next Roles

Technical Lead - ML Infrastructure Principal Engineer - AI Systems Engineering Manager - ML Platforms Chief Technology Officer (startup track)

Company Overview

CentML

CentML is a well-funded AI infrastructure startup focusing on optimizing ML model development and deployment costs. Led by recognized experts in ML systems with strong academic and industry backgrounds.

Early-stage startup with strong technical foundation and competitive positioning in the growing ML infrastructure optimization market
Based in the San Francisco Bay Area, positioning the company within the primary hub of AI technology development and talent
Research-driven engineering culture with emphasis on technical excellence and innovation, typical of leading AI infrastructure startups
Advertisement
Ad Space
Apply Now

Data Sources & Analysis Information

Job Listings Data

The job listings displayed on this platform are sourced through BrightData's comprehensive API, ensuring up-to-date and accurate job market information.

Sauge AI Market Intelligence

Our advanced AI system analyzes each job listing to provide valuable insights including:

  • Industry trends and market dynamics
  • Salary estimates and market demand analysis
  • Role significance and career growth potential
  • Critical success factors and key skills
  • Unique aspects of each position

This integration of reliable job data with AI-powered analysis helps provide you with comprehensive insights for making informed career decisions.