Staff Machine Learning Engineer, Consumer Risk AI

Location: Mountain View, California, United States

Base pay: $202,500–$274,000 per year

Company

hirify.global is a financial technology platform operating products including TurboTax, Credit Karma, QuickBooks, and Mailchimp.

What you will do

• Own the architecture and technical direction of the consumer risk data and model-serving platform.
• Design multi-cloud infrastructure, federated account-link mapping, and governed datasets in the central data lake.
• Build streaming and batch feature infrastructure with monitoring for drift and staleness.
• Develop evaluation frameworks for model quality, regression, and production impact.
• Own real-time model deployment and decision-engine integration within a sub-second latency budget.
• Establish engineering standards, automate model-lifecycle workflows, mentor engineers, and create reusable reference patterns.

Requirements

• 8+ years of production software engineering experience, including substantial work on ML systems and cross-team engineering leadership.
• Strong foundations in data structures, algorithms, distributed systems, system design, and applied machine learning.
• Proficiency in Python and SQL, with production experience in Spark, Flink, or equivalent streaming and batch technologies.
• Experience owning a data or ML platform used by multiple teams and operating it after launch.
• Experience deploying real-time models under strict latency requirements.
• Cloud infrastructure experience, ideally AWS with SageMaker or equivalent ML tooling, including infrastructure as code, CI/CD, and cost ownership.

Nice to have

• Experience in risk, fraud, payments, credit, or real-time decisioning.
• Feature stores, entity resolution, identity graphs, rules engines, or model-to-decision handoffs.
• Regulated data handling, field-level encryption, fine-grained access control, and financial-services data governance.
• Experience orchestrating AI agents with deterministic engineering guardrails.

Culture & Benefits

• Competitive compensation with pay-for-performance rewards.
• Potential eligibility for cash bonuses, equity rewards, and benefits under applicable plans.
• Focus on operational excellence, reproducibility, observability, and cross-team collaboration.