Build reusable AI platform capabilities that engineering and business teams can adopt safely. Deliver shared inference integrations, retrieval pipelines, evaluations, and self-service tools.
Compensation
$115,000—$175,000 USD
Responsibilities
Build reusable inference interfaces and self-service templates with least-privilege identities, approved tool permissions, documented failure behavior, and human approval gates.
Partner with data engineers to deliver retrieval pipelines that preserve source permissions, provenance, refresh behavior, and approved ingestion and retrieval constraints.
Create versioned evaluations and release gates covering regressions, permission leakage, prompt injection, and tool safeguards, with recorded results and rollback procedures.
Monitor failures, latency, usage, and costs; maintain privacy-aware traces, configurations, runbooks, and incident improvements for supported shared AI services.
Agree interfaces with application and data teams, implement Security-approved controls, and leave business decisions and use-case acceptance with accountable owners.
As a Senior Principal Data Architect, provide specialized expertise to influence target state architecture and strategy, building a Data Services platform that connects scattered assurance data into one governed model.
Compensation
competitive total rewards package including base salary determined based on the role, experience, skill set and location.
Responsibilities
Own the design of the enterprise semantic model. This means stable entity identities, typed relationships, constraints and versioning across the chain Control Objective → Procedure → Application → Platform → Evidence → Finding → Closure.
Set and enforce firmwide standards for modelling, identity resolution, taxonomy and ontology.
Work with function SMEs (for example IGA and PAM) to register domain taxonomies against a common core.
Decide when to use graph-native processing and when to use lakehouse/SQL processing for traversal, impact analysis and graph analytics at scale.
Design how the semantic layer fits with the data lakehouse (Databricks bronze, silver and gold layers).
Requirements
Nice to have: risk, audit or compliance data deesign
Nice to have: identity and access management (IGA, PAM)
Help secure 439M accounts and $1.8T in annual payment volume by designing, building, and operating automated security scanners and developer guardrails across the software development lifecycle.
Locations (2)
Austin, Texas, United States
Chicago, Illinois, United States
Compensation
The expected range of pay for this role by location is: Primary Location | Pay Range: Austin, Texas: ($130,500.00 - $193,600.00 Annually) Additional Location(s) | Pay Range: Chicago, Illinois: ($130,500.00 - $193,600.00 Annually) Additional compensation for this role may include an annual performance bonus, equity, or other incentive compensation, as applicable.
Responsibilities
Independently apply security best practices to enhance and optimize systems
Partner with peers and internal teams to drive security initiatives
Analyze and resolve security challenges by adapting standard processes
Influence the quality, efficiency, and effectiveness of the team
Collaborate with other engineers to gather and incorporate feedback
Provide operational and technical support to National & Navy missions, ensuring confidentiality, integrity and availability of systems, networks, and data.
Ensure the confidentiality, integrity and availability of systems, networks, and data through planning, analysis, development, implementation, maintenance, and enhancement of information systems security program, policies, procedures, and tools.
Support cybersecurity operations for IT networks, systems, and cloud platforms by applying cybersecurity policies and security engineering principles.
Administer enterprise security tools, including firewalls, zero-trust technologies, logging and metrics systems.
Ensure necessary security controls are in place and operate as intended; advising users of risks to systems and assessing risk; completing and maintaining appropriate security documentation.
Own the product capability that lets PhysicsX and its customers improve LLMs for their specific work, turning post‑training methods into usable solutions.
Responsibilities
Own the path from a base model and customer workflow to a measured, deployable model improvement
Build reliable post‑training harnesses covering data preparation, training, evaluation, model artifacts and hand‑off to serving
Improve open models against PhysicsX's agent and engineering benchmarks
Develop and operate methods spanning SFT, preference optimisation, RLHF/RLAIF, RL with verifiable rewards, distillation, rejection sampling and related techniques
Build reproducible synthetic‑data and expert‑data pipelines
Requirements
Nice to have: post‑training open‑weight models for tool use, reasoning or long‑horizon agent workflows
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