Machine Learning Engineer

Proofpoint•Sunnyvale, CA

12d•Hybrid

About The Position

Proofpoint is building the next generation of AI-powered security systems to protect organizations from rapidly evolving threats. We are looking for a highly skilled Machine Learning Engineer to help design and build production-grade ML systems that power intelligent security detections at scale. This role is focused on developing and deploying fine-tuned models that are small, fast, reliable, and cost-efficient — capable of operating in real-world, high-throughput security environments. You will work closely with security researchers, platform engineers, and product teams to turn cutting-edge ML techniques into robust detection capabilities used in production. If you enjoy optimizing models for latency, efficiency, inference cost, and operational reliability — while solving difficult security problems — we’d love to talk to you.

Requirements

2+ years of experience in Machine Learning Engineering or Applied AI
Strong experience building and deploying ML systems in production
Experience fine-tuning transformer models or LLMs
Strong Python engineering skills
Experience optimizing models for inference efficiency and scale
Solid understanding of model evaluation, experimentation, data pipelines, distributed training
Experience deploying models in cloud or containerized environments
Strong software engineering fundamentals and production mindset

Nice To Haves

Experience with modern ML frameworks such as PyTorch, Hugging Face, TensorFlow (optional)

Responsibilities

Design, train, fine-tune, and evaluate machine learning models for security detection use cases
Build lightweight, high-performance models optimized for low latency, low inference cost, high throughput, operational reliability
Develop fine-tuning pipelines for LLMs and smaller transformer-based models
Experiment with techniques such as distillation, quantization, pruning, retrieval augmentation, parameter-efficient fine tuning (LoRA, adapters, etc.)
Improve detection quality while minimizing false positives and false negatives
Build scalable ML infrastructure and production inference pipelines
Partner with security researchers to transform detection logic into ML-powered systems
Measure and optimize model performance across quality, speed, memory footprint, and cost
Contribute to data engineering and labeling workflows for supervised training
Monitor production models and continuously improve robustness and reliability.

Benefits

Competitive compensation
Comprehensive benefits
Career success on your terms
Flexible work environment
Annual wellness and community outreach days
Always on recognition for your contributions
Global collaboration and networking opportunities
flexible time off
a comprehensive well-being program with two paid Wellbeing Days and two paid Volunteer Days per year
a three-week Work from Anywhere option