Staff Machine Learning Engineer

Responsibilities

• Build and deploy end-to-end machine learning pipelines covering data curation, training or fine-tuning, evaluation, and high-scale serving.
• Develop deep learning models for security use cases, including transformer-based classifiers, embedding models, and sequence models.
• Adapt, fine-tune, and productionize open-weight LLMs and small language models using LoRA, QLoRA, instruction tuning, and distillation.
• Optimize models for production using quantization, batching, and high-throughput serving.
• Architect and operationalize robust ML services across AWS and GCP using cloud-native service architectures.
• Collaborate with engineering teams to execute organizational AI strategies and solve large-scale technical challenges.

Requirements

• Bachelor’s or advanced degree in Computer Science, Machine Learning, Mathematics, Physics, Statistics, Engineering, or a related field.
• At least 2 years of applied machine learning experience.
• Experience using modern AI/ML frameworks and foundational model workflows to design, train, and deploy intelligent systems at scale.
• Strong understanding of machine learning fundamentals, including loss functions, optimization, regularization, evaluation metrics, and imbalanced or noisy data.
• Strong Python programming skills and hands-on experience with PyTorch, TensorFlow, or JAX.
• Experience training or fine-tuning LLMs and deploying deep learning models such as transformers, embedding models, and generative AI architectures in production.
• Experience with generative AI architectures or scalable inference optimization is preferred.
• Peer-reviewed publications or prominent open-source contributions in modern deep learning are preferred.

Benefits

• Remote position within the United States, with a hybrid preference for Santa Clara, California.
• Full-time position with various health plans, vacation and sick time, parental leave, retirement options, education reimbursement, and in-office perks.
• Inclusive workplace with reasonable support for candidates requiring accommodations or adjustments.