Staff Machine Learning Engineer (ML Platform)

Location: Remote - US. Remote-first work with in-person company offsites at least twice a year.

Salary: $220K–$265K, plus equity and bonus.

Company

hirify.global builds mobile-first financial products and machine learning-powered credit models using more than 250 real-time financial signals to expand access to credit.

What you will do

• Run and evolve the managed feature store, including feature definition, backfilling, serving, versioning, lineage, testing, and documentation.
• Build automated training, retraining, deployment, and model-promotion platforms that enable data scientists to work without an engineer in the loop.
• Develop self-service agents and tooling for feature registration, training jobs, model promotion, and monitoring.
• Scale batch scoring and low-latency online inference with autoscaling, resource isolation, latency controls, and cost management.
• Extend monitoring for data drift, feature drift, prediction quality, and serving health, with usable alerts and dashboards.
• Partner with data scientists, backend engineers, and stakeholders to identify platform pain points, define standards, and drive adoption across teams.

Requirements

• Deep hands-on experience as a platform or software engineer working in machine learning, with a record of building platforms serving many production models across multiple teams.
• Experience with feature stores and model deployment platforms such as Databricks Feature Store, Feast, Tecton, or equivalent; experience with a managed feature store is especially relevant.
• Strong ML CI/CD experience with GitHub Actions, Jenkins, Azure Pipelines, or similar, plus model registries and infrastructure as code with Terraform.
• Cloud expertise in AWS or Azure for ML deployment, together with Kubernetes and workflow orchestration.
• Strong understanding of batch and real-time serving, autoscaling, and latency and cost trade-offs.
• Experience building self-service tooling adopted by data scientists and working closely with backend engineers to deploy models.
• Ability to gather requirements independently, work through ambiguity, propose solutions, and drive projects to completion.
• Sufficient depth in model development and common algorithms to act as a technical partner to data scientists.

Nice to have

• Familiarity with PySpark, Databricks, and monitoring tools such as Evidently AI.

Culture & Benefits

• Virtual-first collaboration across 14 countries and 12 time zones, with work-from-home office reimbursement.
• Competitive compensation with equity and flexible health plans with substantial subsidies.
• Direct exposure to leadership and growth opportunities based on contributions rather than fixed promotion timelines.
• Paid global company offsites held twice a year.
• Respectful, collaborative culture focused on excellence, impact, learning from mistakes, and expanding access to fair credit.

Hiring process

• Recruiter call, hiring manager interview, skills panel, and final leadership conversation.