Tech Lead Manager (MLRE, ML Systems)

Scale AI; New York (NY); On-site; USD 160,000 - 210,000; Full time

Benefits offered by this job

Health coverage

Dental and vision insurance

Mental healthcare services

Learning & development stipend

ERGs and community events

Job summary

Scale AI's LLM post-training platform team builds our internal distributed framework for large language model training, enabling fast and reliable training and evaluation for ML researchers and data scientists.

You will build, profile and optimize the training and inference framework, collaborate with ML and research teams to accelerate R&D, and integrate state-of-the-art technologies to optimize our ML system.

Qualifications

• Strong software engineering skills with ML framework experience.
• Experience building and optimizing large-scale ML systems.
• Practical knowledge of post-training methods like RLHF/RLVR and related algorithms.
• Ability to collaborate across ML and research teams to drive platform improvements.

Responsibilities

• Build, profile and optimize our training and inference framework.
• Collaborate with ML and research teams to accelerate R&D and data curation initiatives.
• Research and integrate state-of-the-art technologies to optimize our ML system.

Skills

System optimization

Distributed ML systems

Multi-node training

RLHF/RLVR

PPO/GRPO

Communication

Model training/inference

Tools

CUDA

PyTorch

Transformers

Flash attention

Job description

• Scale’s LLM post-training platform team builds our internal distributed framework for large language model training. The platform powers MLEs, researchers, data scientists, and operators for fast and automatic training and evaluation of LLMs. It also serves as the underlying training framework for the data quality evaluation pipeline
• Scale is uniquely positioned at the heart of the field of AI as an indispensable provider of training and evaluation data and end-to-end solutions for the ML lifecycle. You will work closely with Scale’s ML teams and researchers to build the foundation platform which supports all our ML research and development works. You will be building and optimizing the platform to enable our next generation LLM training, inference and data curation
• Build, profile and optimize our training and inference framework
• Collaborate with ML and research teams to accelerate their research and development, and enable them to develop the next generation of models and data curation
• Research and integrate state-of-the-art technologies to optimize our ML system

Benefits

• Health & Wellbeing: Our holistic approach to supporting Scaliens includes comprehensive health coverage, dental and vision insurance, mental healthcare services, and more. PTO policies and accommodating schedules ensure you’ll get time off when you need it to relax and recharge. Note that our offerings may vary by region as we strive to respond to the unique needs of Scaliens around the globe.
• Personal & Career Growth: Continuously learn and grow through annual learning & development stipend, attending leadership breakfasts, manager training, speaker series, and joining an ERG.
• Building Scale Community: We welcome guests to our offices, and you can expect to see Scalien families and friends around. Join local happy hours, and accept invites to game nights, book clubs, and many other employee-led community events.
• Parental Support: Balancing work and family is essential, and Scale understands the importance of having adequate leave policies in place to promote a healthy home and work life.

Strong software engineering skills, proficient in frameworks and tools such as CUDA, Pytorch, transformers, flash attention, etcExperience with post-training methods like RLHF/RLVR and related algorithms like PPO/GRPO etcPassionate about system optimizationExperience with developing large-scale distributed ML systemsStrong written and verbal communication skills to operate in a cross functional team environmentExperience with multi-node LLM training and inferenceDemonstrated expertise in post-training methods and/or next generation use cases for large language models including instruction tuning, RLHF, tool use, reasoning, agents, and multimodal, etc