Senior Machine Learning Engineer Job York area,York and North Yorkshire England UK,Software Development

The Security and Risk Engineering organization builds scalable, AI-powered security solutions that reduce risk and protect Service Now and its customers
We value AI-first thinking, clean architecture, intuitive experiences, and a culture of continuous learning
This is a zero-to-one incubation. We’re building a new class of exposure analysis that ranks security work by exploitability—where an attacker could realistically get in—rather than raw severity. The architecture is evolving, and this role helps define what good looks like
As a Senior Staff Engineer, you own the architecture of an security harness with novel exploitability engine end to end, and you’re accountable for the decisions that shape everything downstream
You set technical direction, make the hard calls defensible, and multiply the engineers around you
The end-to-end architecture of the exploitability engine—from evidence ingestion and entity resolution, through the attack-path probability core and choke-point ranking, to the validation loop that keeps predictions honest
The decisions that cascade through the system: calibrated probability versus ordinal rank, identity as a first-class graph edge, assume-breach seeding, and how the most critical assets are defined. These are model-shaping calls, not implementation details
The probabilistic ranking core: edge-traversal probability, guided path search with hop and likelihood limits, correlated-control-failure modeling, and honest uncertainty bands
The calibration and validation loop—canaries, purple-team and incident replay, calibration measured by zone and vector—that turns modeled weights into evidence rather than opinion
Make-or-break metrics as first-class engineering targets, starting with entity-resolution accuracy and calibration quality
The build-on strategy—extending the existing portfolio rather than rebuilding it, and knowing precisely what to reuse and what must be net-new
Lead zero-to-one work at production scale: turn an ambiguous, novel problem into a reliable system other teams build on, and set the bar where no precedent exists
Drive technical direction across architecture, design, and code reviews, and raise the engineering bar across the incubation
Mentor senior engineers and lead by influence, not title
Partner with product, security R&D, Sec Ops to turn customer problems into architecture, and translate that architecture into decisions leaders can act on
Establish AI safety, security, governance, and guardrails for agentic systems running in production

Benefits

Generous family leave
Matched donations
Annual learning stipends
Flexible PTO
Competitive retirement plan
Paid volunteer time

Experience with AI evaluation, safety, governance, or policy guardrails is a plus
A track record of owning architecture across a large system or multiple teams, with deep experience operating production-quality software
Technical leadership and mentorship that moves teams through influence
Demonstrated experience as the technical owner or lead for a major system or across teams
Strong programming experience in Python and/or Java, Go, or a similar language
Cybersecurity or security-product experience, or familiarity with modern security architectures and operations, is strongly preferred
Command of distributed systems, APIs, cloud-native development, and data or graph systems
Hands-on depth in both agentic and LLM systems and probabilistic or ML-driven scoring—graph modeling, calibration, search and optimization, or risk and probability engineering
Proven zero-to-one at scale: you’ve taken an ambiguous problem to a reliable production system that others depend on
Applied interest in security problems—attack-path analysis, vulnerability management, identity security, threat intelligence, or detection…