Senior Staff Machine Learning Engineer

Description

The Security and Risk Engineering group builds scalable, AI-driven security tools that lower risk for the company and its customers.

This is a zero-to-one incubation. The team is building a new kind of exposure analysis that ranks security issues by how realistically an attacker could exploit them, not by raw severity. The architecture is still changing, and this role will help set its direction.

Responsibilities

• As a Senior Staff Engineer, you own the architecture of a security harness with a new exploitability engine, from start to finish. Your decisions shape everything built on top of it, so you set technical direction, make difficult calls you can defend, and help the engineers around you do more.
• Own the full architecture of the exploitability engine, covering evidence ingestion, entity resolution, the attack-path probability core, choke-point ranking, and the validation loop that keeps predictions accurate.
• Make the model-shaping decisions that affect the whole system: calibrated probability versus ordinal rank, identity as a first-class graph edge, assume-breach seeding, and how the most critical assets are defined.
• Build the probabilistic ranking core: edge-traversal probability, guided path search with limits on hops and likelihood, modeling of correlated control failures, and honest uncertainty bands.
• Design the calibration and validation loop using canaries, purple-team exercises, incident replay, and calibration measured by zone and attack vector, so that modeled weights rest on evidence.
• Treat the make-or-break metrics, starting with entity-resolution accuracy and calibration quality, as primary engineering targets.
• Choose the build-on strategy: extend the existing portfolio instead of rebuilding it, and be clear about what to reuse and what must be new.
• Lead zero-to-one work at production scale. Turn an unclear, novel problem into a reliable system other teams can depend on, and set standards where none exist yet.
• Guide technical direction through architecture, design, and code reviews, and raise the engineering bar across the incubation.
• Mentor senior engineers and lead through influence rather than title.
• Work with product, security R&D, and SecOps to turn customer problems into architecture, and turn that architecture into decisions leaders can act on.
• Set up AI safety, security, governance, and guardrails for agentic systems running in production.

Requirements

• 10+ years of software engineering experience, including leading the design and delivery of complex production systems.
• Demonstrated experience as the technical owner or lead of a major system or of work spanning several teams.
• Track record of owning architecture across a large system or multiple teams, with deep experience running production-quality software.
• Depth in building AI and ML-powered production systems. Probabilistic modeling, graph analytics, or calibration and evaluation is a strong plus.
• Modern AI experience with LLMs, RAG, embeddings, vector search, agentic harnesses and workflows, model evaluation, or AI observability.
• Hands-on depth in agentic and LLM systems and in probabilistic or ML-driven scoring, such as graph modeling, calibration, search and optimization, or risk and probability engineering.
• Proven zero-to-one delivery: you have taken an ambiguous problem to a reliable production system that others rely on.
• Judgment to make consequential architecture decisions under uncertainty and defend them to both engineers and executives.
• Strong command of distributed systems, APIs, cloud-native development, and data or graph systems.
• Strong programming in Python and/or Java, Go, or a similar language.
• Technical leadership and mentorship that moves teams forward through influence.
• Primary skills: predictive modeling, graph ML, security analytics, anomaly detection, risk scoring, LLMs and agentic AI, and MLOps. These point to advanced ML and AI engineering more than conventional software development.
• Bachelor's or Master's degree in Computer Science, Artificial Intelligence, Machine Learning, or a related technical field, or equivalent practical experience.
• Cybersecurity or security-product experience, or familiarity with modern security architectures and operations, is strongly preferred.
• Applied interest in security problems such as attack-path analysis, vulnerability management, identity security, threat intelligence, or detection and response is strongly preferred.
• Experience with AI evaluation, safety, governance, or policy guardrails is a plus.
• Experience with AI-assisted development tools and coding agents such as Claude Code, Codex, Cursor, or Windsurf is a plus.

Conditions

Base pay in this location is $201,300 - $352,300. Equity (where applicable) and variable or incentive pay are also part of the package.

Individual total compensation depends on qualifications, skill level, competencies, and work location, so the base pay shown is a guideline. Pay is set by the geographic location where the role sits and may change with it.

Work persona is flexible, remote, or required in office, assigned based on the nature of the work and the assigned 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, plus an employee stock purchase plan (ESPP).
• Matching donations.
• Flexible time away and family leave programs.