ML Systems Architect & Platform Lead

Sierracorp; San Francisco (CA); On-site; USD 150,000 - 200,000; Full time

Job summary

Sierracorp in San Francisco is seeking a Machine Learning Architect to define the ML strategy and build scalable systems. The role involves architecting end-to-end ML systems, leading technical roadmaps, and mentoring engineers. Ideal candidates have over 10 years of experience in ML engineering, deep expertise across multiple domains, and strong command of tools like PyTorch and JAX. A focus on communication and leadership is essential. Experience in ML platforms and distributed training is highly valued. Join a forward-thinking team shaping the future of AI.

Qualifications

• 10+ years of professional ML engineering or research engineering experience.
• Deep expertise in at least two ML domains, e.g., NLP and computer vision.
• Leadership experience, including tech lead or staff engineer roles.
• Exceptional communication skills for explaining ML concepts.

Responsibilities

• Architect end-to-end ML systems for scale and reliability.
• Define and drive the ML technical roadmap with leadership.
• Lead research spikes on emerging techniques for production viability.
• Establish ML engineering best practices including monitoring.
• Own the ML platform including the feature store and deployment pipelines.

Skills

ML engineering experience

NLP expertise

Computer vision expertise

Hands-on with large language models

PyTorch

JAX

Distributed training knowledge

Exceptional communication skills

Tools

HuggingFace

Job description

Sierracorp in San Francisco is seeking a Machine Learning Architect to define the ML strategy and build scalable systems. The role involves architecting end-to-end ML systems, leading technical roadmaps, and mentoring engineers. Ideal candidates have over 10 years of experience in ML engineering, deep expertise across multiple domains, and strong command of tools like PyTorch and JAX. A focus on communication and leadership is essential. Experience in ML platforms and distributed training is highly valued. Join a forward-thinking team shaping the future of AI.