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Jobs via Dice · 5 hours ago

Senior ML Engineer (Internet Security)

Santa Clara, CA

Full-time

Onsite

Mid, Senior Level

$205K/yr - $235K/yr

2+ years exp

Palo Alto Networks is a cybersecurity company dedicated to protecting the digital way of life. The Senior ML Engineer will build machine learning models and develop big data systems to analyze and categorize URLs, playing a key role in transforming ideas into products for the next generation security platform.

Computer Software

Responsibilities

Design, build, and operate production machine learning systems that balance model quality, cost, latency, and reliability in a security-sensitive environment

Own the end-to-end lifecycle of ML and LLM components, from problem formulation and model development to production deployment, monitoring, and iterative improvement

Integrate ML and LLM-based services with backend systems and data pipelines, ensuring scalability, observability, and safe operation in production

Develop and maintain automated training, evaluation, and retraining pipelines, and build data analysis tools to continuously improve model performance as data and threats evolve

Partner closely with Product Managers and domain experts to translate product and security requirements into robust ML solutions with clear success metrics

Collaborate with software engineers and SREs on release planning, deployment strategies, monitoring, and incident response to ensure reliable and predictable production behavior

Qualification

Machine LearningDeep LearningLarge Language ModelsMLOpsPythonCloud PlatformsData AnalysisCollaborationCommunicationProblem Solving

Required

Strong problem solver with collaborative team player with clear communication skills, able to work effectively across engineering, product, and SRE teams

Solid foundation in Machine Learning, Deep Learning, and NLP, with hands-on experience using modern architectures such as transformer-based models and representation learning techniques

Practical experience applying Large Language Models (LLMs) to real-world problems, including text understanding, classification, extraction, summarization, or reasoning over large-scale and noisy data

Experience designing, implementing, and operating LLM-powered components in production, including prompt design, model adaptation or fine-tuning, evaluation, and cost/performance optimization

Familiarity with AI agent-based approaches, such as multi-step inference pipelines, tool-augmented LLM workflows, or systems that combine models, heuristics, and external signals to drive reliable decisions

Experience with MLOps / AIOps practices for operating ML and LLM systems in production, including model lifecycle management, monitoring, logging, alerting, retraining workflows, and debugging production issues

Understanding of model quality, robustness, and safety considerations, including evaluation methodologies, failure modes, and guardrails required for production ML systems in security-sensitive environments

Strong experience with ML frameworks, libraries, and tooling (e.g., PyTorch, Tensorflow, Keras, Scikit-learn, Kubeflow), and solid software engineering fundamentals

Ability to independently own ML features end-to-end, from problem formulation and system design to implementation, deployment, and iterative improvement in production

Proficient in Python, working knowledge of Java, Linux, and shell scripting

Experience building and operating services on cloud platforms (Google Cloud Platform and/or AWS) and in containerized environments (Docker, Kubernetes)

Familiarity with relational and NoSQL data stores such as MySQL, MongoDB, or similar systems

MS or Ph.D. in Computer Science or a related field, with a focus on Machine Learning, and 2+ years of industry experience delivering ML systems in production environments