Machine Learning Engineer
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Job Description
Machine Learning Engineer building efficient, production-grade ML models for Proofpoint’s AI-powered cybersecurity detections. Optimizing transformer and LLM systems for reliable, scalable threat protection.
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, and operational reliability
- Develop fine-tuning pipelines for LLMs and smaller transformer-based models
- Experiment with distillation, quantization, pruning, retrieval augmentation, and parameter-efficient fine-tuning techniques such as LoRA and adapters
- 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
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 with modern ML frameworks such as PyTorch and Hugging Face; TensorFlow optional
- Experience optimizing models for inference efficiency and scale
- Solid understanding of model evaluation, experimentation, data pipelines, and distributed training
- Experience deploying models in cloud or containerized environments
- Strong software engineering fundamentals and production mindset
Benefits:
- Competitive compensation
- Comprehensive benefits
- Flexible work environment
- Annual wellness and community outreach days
- Always on recognition for your contributions
- Global collaboration and networking opportunities
- Flexible time off
- Comprehensive well-being program
- Two paid Wellbeing Days per year
- Two paid Volunteer Days per year
- Three-week Work from Anywhere option
- Variable compensation and/or equity may be available
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