Principal Machine Learning Engineer

Description

The Security and Risk Engineering organization builds AI-powered security solutions that lower risk for the company and its customers.

This is a zero-to-one incubation of exposure analysis that ranks security work by exploitability, meaning how realistically an attacker could get in, instead of by raw severity. The architecture is still taking shape, and this role helps decide what it should become.

Responsibilities

• Set the technical vision for exploitability-based security across the whole portfolio, not only one engine.
• Define the hardest modeling problems worth solving and set the direction that staff and senior engineers work within.
• Shape where the engine goes next and which problem classes it should cover beyond any single release.
• Work on open modeling questions: keeping calibrated attack-path probability reliable across environments, bringing identity and agent surfaces into the model, and feeding ground truth back into it.
• Define engineering standards and architecture that several teams follow, covering scalability, reliability and the scientific rigor of scoring.
• Decide what the engine reuses from existing products, what must be built new, and why.
• Drive technical direction across multiple teams without direct authority, and turn ambitious ideas into enterprise-grade products.
• Explore new ways to apply emerging AI to cybersecurity, rather than adding AI to existing products as an afterthought.
• Present the team's technology and direction to executives, customers, partners and the wider engineering organization.
• Mentor staff and senior engineers and raise the engineering bar through coaching and technical leadership.
• Lead AI-native engineering practices, including heavy use of coding agents and autonomous development, testing, evaluation and operations.
• Set direction for AI safety, security, governance and guardrails for agentic systems in production.

Requirements

• Deep knowledge of modern AI and ML, with a record of shipping production AI systems using LLMs, foundation models, agentic architectures, RAG, retrieval and model evaluation, plus probabilistic or ML-driven scoring.
• Ability to set a clear technical vision, get several teams behind it, and then go deep into architecture and code.
• A proven track record of taking ambiguous, ambitious ideas to products that run at enterprise scale.
• An inventive mindset that questions standard approaches and acts on the chances that fast-moving AI creates.
• Strong grasp of distributed systems, APIs, cloud-native platforms, and data or graph systems.
• Expert Python and current AI frameworks and infrastructure. Experience with Java, Go or similar languages is valuable.
• Executive-level communication that can convey a technical vision to engineers, customers and senior leaders.
• Applied depth in security problems such as threat detection, vulnerability and exposure management, identity security, risk prioritization or autonomous remediation is strongly preferred.
• Extensive hands-on use of AI-native development tools and coding agents such as Claude Code, Codex, Cursor or Windsurf across the development lifecycle.
• 15+ years of software engineering experience, including significant technical and engineering leadership.
• Primary skills are predictive modeling, graph ML, security analytics, anomaly detection, risk scoring, LLMs and agentic AI, and MLOps. These reflect advanced ML and AI engineering more than traditional software development.
• Track record of designing and delivering AI and ML products and platforms in production.
• Experience leading technical initiatives across multiple teams without direct authority.
• Ability to design systems that scale to enterprise workloads.
• Hands-on work with frontier LLMs, agent frameworks, retrieval and vector technologies, and model evaluation and observability. Probabilistic modeling or graph analytics is a strong plus.
• Strong backend experience with distributed systems, APIs, microservices and cloud-native architectures.
• Bachelor's or Master's degree in Computer Science, Artificial Intelligence, Machine Learning or a related technical field, or equivalent practical experience.
• Experience with cybersecurity products or solid familiarity with modern security architectures and security operations is strongly preferred.

Conditions

Base pay for this location is $240,100 - $420,200. The role may also include equity where applicable, variable or incentive pay, and benefits.

Base pay shown is a guideline. Total compensation depends on qualifications, skill level, competencies and work location.

Pay depends on the geographic location where the role sits and may change with work location.

Work persona (flexible, remote or required in office) is assigned by the nature of the work and work location. The company may use a third-party service to check the distance between your home and the nearest office.

Benefits

• Health plans, including flexible spending accounts.
• 401(k) plan with company match, ESPP, matching donations, flexible time away and family leave programs.