Senior Electronics Hardware Engineer

Windsor Troy Law, LLP; San Francisco (CA); On-site; USD 160,000 - 230,000; Full time

Job summary

Join a pioneering company in AI infrastructure as a Training Dataset and Checkpoint Optimization Engineer. This role focuses on enhancing data pipelines and checkpointing systems for large-scale machine learning workloads. You will collaborate with talented ML researchers and engineers to ensure efficient, scalable training workflows. Your expertise in data engineering and distributed systems will be pivotal in optimizing performance and reliability, making a significant impact in the AI domain. If you're passionate about cutting-edge technology and eager to tackle complex challenges, this opportunity is perfect for you.

Benefits

Startup equity

Health insurance

Competitive benefits

Qualifications

• 5+ years in data engineering or distributed systems required.
• Expertise in high-performance data processing libraries is essential.

Responsibilities

• Design and optimize high-throughput data pipelines for training datasets.
• Build and optimize distributed checkpoint mechanisms for training workflows.

Skills

Data Engineering

Distributed Systems

Machine Learning Infrastructure

Analytical Skills

Problem-Solving

Communication Skills

Tools

PyTorch DataLoader

TensorFlow Data

DALI

Parquet

HDF5

POSIX

Lustre

GPFS

Job description

LLM Training Dataset and Checkpoint Optimization Engineer

Be among the first applicants.

USD 200,000 - 250,000

2 days ago

About Us

Together.ai is a leader in developing AI infrastructure that powers the training of state-of-the-art models. We focus on creating scalable, efficient systems for handling massive datasets and managing large-scale distributed checkpoints, ensuring seamless workflows for training and fine-tuning AI models.

We are seeking a Training Dataset and Checkpoint Acceleration Engineer to optimize data pipelines and checkpoint mechanisms for large-scale machine learning workloads. In this role, you will work at the intersection of data engineering and distributed systems, ensuring that training workflows are highly performant, reliable, and cost-efficient.

Responsibilities

• Dataset Acceleration:
• Design and optimize high-throughput data pipelines for streaming and processing massive training datasets.
• Implement caching, sharding, and prefetching techniques to maximize data-loading efficiency.
• Ensure efficient integration with distributed storage systems (e.g., S3, GCS, Lustre, Ceph).
• Checkpointing Systems:
• Build and optimize distributed checkpoint mechanisms for large-scale training workflows.
• Implement techniques to minimize checkpoint I/O overhead and ensure fault tolerance.
• Develop incremental and differential checkpointing solutions to reduce storage costs.
• Performance Optimization:
• Profile and debug bottlenecks in data pipelines and checkpoint systems.
• Optimize for GPU/TPU utilization by ensuring efficient data feeding and checkpoint recovery times.
• Scalability and Reliability:
• Develop systems that scale efficiently across thousands of nodes and petabyte-scale datasets.
• Ensure fault-tolerant recovery and resume mechanisms for long-running training jobs.
• Collaboration and Support:
• Work closely with ML researchers, data engineers, and infrastructure teams to understand workload requirements.
• Build tools and frameworks to enable seamless integration of dataset and checkpointing systems with existing ML workflows.

Qualifications

Must-Have:

• Experience: 5+ years of experience in data engineering, distributed systems, or ML infrastructure.
• Technical Skills: Expertise in high-performance data processing libraries (e.g., PyTorch DataLoader, TensorFlow Data, DALI); Proficiency in distributed storage systems and data formats (e.g., Parquet, HDF5); Strong understanding of checkpointing frameworks and file systems (e.g., POSIX, Lustre, GPFS).
• Programming: Proficient in Python, C++, or Go for performance-critical systems; Experience with I/O optimization techniques (e.g., asynchronous data loading, prefetching); Familiarity with compression and serialization for large datasets and checkpoints.
• Soft Skills: Analytical and problem-solving mindset; Strong communication and collaboration skills across teams.

Nice-to-Have:

• Experience with ML frameworks (e.g., PyTorch, TensorFlow, JAX) and distributed training.
• Familiarity with hardware accelerators (e.g., GPUs, TPUs) and storage optimizations.
• Knowledge of open-source contributions or projects related to data pipelines or checkpointing.
• Experience with incremental and real-time checkpointing solutions.

Compensation

We offer competitive compensation, startup equity, health insurance, and other competitive benefits. The US base salary range for this full-time position is: $160,000 - $230,000 + equity + benefits. Our salary ranges are determined by location, level, and role. Individual compensation will be determined by experience, skills, and job-related knowledge.

Together AI is an Equal Opportunity Employer and is proud to offer equal employment opportunity to everyone regardless of race, color, ancestry, religion, sex, national origin, sexual orientation, age, citizenship, marital status, disability, gender identity, veteran status, and more.