Applied Machine Learning Engineer (LLMs & RL)

TL;DR

Applied Machine Learning Engineer (LLMs & RL) (AI): Designing and implementing post-training pipelines for large language models with an accent on RL environments, reward models, and distributed training. Focus on scaling distributed workloads, developing evaluation benchmarks, and improving model capabilities for agentic applications.

Location: Hybrid; must be based in Santa Clara, CA; Folsom, CA; or Hillsboro, OR

Salary: $170,500 - $240,710 USD

Company

hirify.global's Client Computing Group (CCG) develops PC products and platforms to deliver purposeful computing experiences.

What you will do

• Design and maintain post-training pipelines from data ingestion through deployment.
• Develop reinforcement learning environments, reward models, and evaluation signals.
• Debug, optimize, and scale distributed training workloads.
• Design and execute research experiments and ablation studies.
• Develop benchmarks and evaluation metrics for model capability and alignment.

Requirements

• Must be based in or able to work from Santa Clara, CA; Folsom, CA; or Hillsboro, OR.
• Bachelor's degree in Computer Science, Electrical Engineering, Mathematics, Statistics, or a related STEM field.
• 3+ years of experience in ML engineering, data science, or ML research and fine-tuning.
• Proficiency in Python and C++ as primary development languages for ML.
• Strong knowledge of LLM architectures, optimization, and fine-tuning evaluation techniques.

Nice to have

• Master's or PhD degrees.
• Experience scaling full post-training pipelines, including SFT and RL.
• Experience building evaluation frameworks that measure model capability and alignment quality.
• Experience with model distillation and quantization.

Culture & Benefits

• Competitive total compensation package including pay, stock bonuses, and benefit programs.
• Comprehensive health, retirement, and vacation benefits.
• Hybrid work model allowing split time between on-site and off-site work.
• Commitment to innovation, collaboration, and ethical hiring practices.