Staff ML Engineer - LLMs, MoEs, On-Device AI Lead

webAI; United States; Remote; USD 180,000 - 240,000; Full time

Benefits offered by this job

Competitive salary

Comprehensive health, dental, and vis

401(k) match

Equity options

Health & Wellness stipend $200/mo

Continuing Education support

Function Health subscription $500/yr

Free parking for in-office employees

Flexible Time Off (FTO)

Parental leave

Supplemental life insurance

Job summary

webAI is seeking a Staff Machine Learning Engineer with a strong background in Large Language Models and MoEs to lead core product initiatives, including on-device inference optimization, quantization, RAG, and agentic framework developments. You will drive end-to-end delivery across teams and mentor junior engineers.

The role emphasizes collaboration with other engineering domains, continuous research, and applying state-of-the-art ML advances to improve platform performance and efficiency.

Qualifications

• Ph.D. in Computer Science or related field required.
• 6+ years in machine learning with LLMs and Mixture of Experts.
• Strong Python and ML framework experience (TensorFlow, PyTorch).

Responsibilities

• Lead development and optimization of Large Language Models and Mixture of Experts models.
• Collaborate with cross-functional teams to integrate ML models into our platform.
• Conduct cutting-edge research in ML to improve performance and efficiency of LLMs.
• Stay abreast of AI advances and apply knowledge to improve models and methodologies.
• Mentor junior engineers and contribute to knowledge sharing and best practices.

Skills

Python programming

Team leadership

Research & experimentation

Problem solving

Education

Ph.D. in Computer Science or related field

Tools

TensorFlow

PyTorch

Docker

Kubernetes

Job description

webAI is seeking a Staff Machine Learning Engineer with a strong background in Large Language Models and MoEs to lead core product initiatives, including on-device inference optimization, quantization, RAG, and agentic framework developments. You will drive end-to-end delivery across teams and mentor junior engineers.

The role emphasizes collaboration with other engineering domains, continuous research, and applying state-of-the-art ML advances to improve platform performance and efficiency.