Machine Learning Engineer

1 day ago

Remote, United States or Sunnyvale, CA, USAMid Level

H1B Sponsor

Responsibilities

Design, train, fine-tune, and evaluate machine learning models for security detection use cases.
Build lightweight, high-performance models optimized for low latency, inference cost, throughput, and operational reliability.
Develop fine-tuning pipelines for LLMs and smaller transformer-based models.
Apply techniques including distillation, quantization, pruning, retrieval augmentation, and parameter-efficient fine-tuning with LoRA or adapters.
Build scalable ML infrastructure and production inference pipelines.
Partner with security researchers to turn detection logic into ML-powered systems.
Measure and optimize model quality, speed, memory footprint, and cost.
Contribute to data engineering and labeling workflows for supervised training.
Monitor production models and improve their robustness and reliability.

At least 2 years of experience in machine learning engineering or applied AI.
Strong experience building and deploying production ML systems.
Experience fine-tuning transformer models or LLMs.
Strong Python engineering skills.
Experience with modern ML frameworks such as PyTorch and Hugging Face; TensorFlow experience is optional.
Experience optimizing models for inference efficiency and scale.
Understanding of model evaluation, experimentation, data pipelines, and distributed training.
Experience deploying models in cloud or containerized environments.
Strong software engineering fundamentals and a production mindset.

Competitive compensation and comprehensive benefits.
Flexible work environment and flexible time off.
Annual wellness and community outreach days, including two paid Wellbeing Days and two paid Volunteer Days per year.
Three-week Work from Anywhere option.
Recognition programs and global collaboration and networking opportunities.

1,001-5,000 employees

Intent-based protection for every human and every AI agent, across all data.