Product Security Engineer - AI at Crusoe

San Francisco, California, United States

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Not SpecifiedCompensation

Senior (5 to 8 years), Expert & Leadership (9+ years)Experience Level

Full TimeJob Type

UnknownVisa

AI, Cloud Infrastructure, TechnologyIndustries

Requirements

3+ years of professional experience building and maintaining production systems, with strong Python programming skills and experience across the stack (backend/frontend)
Deep expertise in advanced Generative AI techniques, including implementing Retrieval-Augmented Generation (RAG), designing AI Agents and Multi-step Cognitive Processes (MCP), and building with workflow orchestration frameworks
Proven ability to own the entire model lifecycle by designing and managing robust MLOps pipelines; experience with containerization (Docker), virtualization (VMs), and cloud platforms (AWS, GCP, Azure) is a plus
Experience in designing, implementing, and fine-tuning custom LLMs, coupled with a strong understanding of NLP fundamentals, transformer architectures, PyTorch/TensorFlow, and data structures
Strong curiosity about security, privacy, and threat modeling; a desire to safely "break" systems to secure them and apply best practices to AI pipelines and deployments
Strong product sense for rapid iteration and refinement based on data, combined with a collaborative mindset to work closely with engineers, product managers, and security analysts in a fast-paced environment

Responsibilities

Act as the technical leader and SME on the practical security of our AI and LLM ecosystem and define the long-term technical roadmap for AI security architecture and drive high-impact cross-functional initiatives
Lead the design and implementation of highly secure Generative AI solutions for security applications, focusing on architectural patterns like Retrieval-Augmented Generation (RAG)
Architect and implement custom, AI-powered security tooling that automates threat detection, vulnerability analysis, and data access control, moving from proof-of-concept to production at scale
Establish governance and processes for secure MLOps pipelines. Define standards for model versioning, deployment, and monitoring, ensuring they meet rigorous compliance and security requirements
Lead threat modeling exercises for novel AI systems. Apply advanced security and privacy best practices, and mentor senior engineers on secure development practices in the GenAI domain
Drive the entire lifecycle of critical AI security projects

Skills

Key technologies and capabilities for this role

AI SecurityLLMRetrieval-Augmented GenerationRAGMLOpsThreat ModelingGenerative AISecurity ArchitectureModel VersioningVulnerability AnalysisData Access Control

Questions & Answers

Common questions about this position

Is this role remote or onsite?

The role is onsite.

What salary or compensation does this position offer?

This information is not specified in the job description.

What skills and experience are required for this Product Security Engineer role?

Candidates need 3+ years of professional experience building production systems with strong Python skills, deep expertise in Generative AI techniques like RAG and AI Agents, experience owning MLOps pipelines, and knowledge of custom LLMs, NLP, transformer architectures, PyTorch/TensorFlow.

What is the company culture like at Crusoe?

Crusoe focuses on driving meaningful innovation, making a tangible impact, and joining a team setting the pace for responsible, transformative cloud infrastructure in the AI revolution with sustainable technology.

What makes a strong candidate for this AI Security Engineer position?

A strong candidate has 3+ years building production systems with Python expertise, deep Generative AI knowledge including RAG and MLOps ownership, and experience with LLMs, NLP, and frameworks like PyTorch/TensorFlow, plus strong curiosity about security.

Crusoe

Utilizes wasted energy for computing power

About Crusoe

Crusoe Energy Systems Inc. provides digital infrastructure that focuses on using wasted, stranded, or clean energy sources to power high-performance computing and artificial intelligence. The company helps clients in the technology and energy sectors by offering scalable computing solutions that aim to reduce greenhouse gas emissions and support the transition to cleaner energy. Crusoe's approach involves converting excess natural gas and renewable energy into computing power, which allows them to maximize resource efficiency while minimizing environmental impact. Unlike many competitors, Crusoe specifically targets the intersection of energy and technology, generating revenue by supplying computing resources to enterprises that need significant computational power for applications like AI and machine learning, along with providing technical support.

Denver, ColoradoHeadquarters

2018Year Founded

$1,082.2MTotal Funding

SERIES_DCompany Stage

Energy, AI & Machine LearningIndustries

201-500Employees

Benefits

Industry competitive pay

Health insurance package options that include HDHP and PPO, vision, and dental for you and your dependents

Paid life insurance, short-term and long-term disability

Parental leave

Stock options in a fast-growing, well-funded technology company

Pet-friendly offices

Teladoc

401(k) with a 4% match

Unlimited time off

Cell phone reimbursement

Tuition reimbursement

Company paid commuter benefit; $100 per month

Calm

Risks

Increased competition in AI infrastructure could threaten Crusoe's market share.

Regulatory scrutiny may arise from Bitcoin mining's environmental concerns.

Rapid expansion into AI infrastructure may lead to operational challenges.

Differentiation

Crusoe converts wasted energy into computing power, reducing environmental impact.

The company offers scalable solutions for AI and high-performance computing needs.

Crusoe's Digital Flare Mitigation technology utilizes natural gas for eco-friendly Bitcoin mining.

Upsides

Crusoe secured $600M in Series D funding, boosting AI infrastructure expansion.

Partnerships with tech firms enhance Crusoe's AI capabilities and market reach.

AI-driven energy optimization can significantly reduce operational costs in data centers.

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