Principal Machine Learning Engineer (ML Platform)

TL;DR

Principal Machine Learning Engineer (ML Platform): Building a standardized production-grade ML platform for delivery-date prediction, fraud detection, and shipping optimizations with an accent on ML lifecycle management, inference serving, and observability. Focus on designing Kubernetes-native model serving, standardizing workflows with MLflow or equivalent, and establishing reliability standards across training, deployment, and operations.

Location: Engineering Remote (United States), anywhere in the US except Delaware, Nevada, Ohio, Oregon, Hawaii, New Mexico, and West Virginia; many roles can be based internationally via Remote.com

Salary: $212k–$287k annual base (midpoint $250k), plus equity and benefits

Company

hirify.global is the shipping layer of the internet, connecting merchants to carriers worldwide through a single API and dashboard for global e-commerce.

What you will do

• Set technical strategy and multi-quarter roadmap for ML platform aligned to business priorities.
• Own cross-team architecture decisions, RFCs, design reviews for ML lifecycle and inference.
• Build core components: ML lifecycle foundation (tracking, registry, versioning), training pipelines, Kubernetes model serving, batch inference, observability.
• Evaluate inference frameworks, resolve performance bottlenecks, establish engineering standards.
• Partner with Data Science and Engineering teams to bridge research to production and drive adoption.
• Mentor engineers through reviews, guidance, and best practices.

Requirements

• 15+ years software engineering experience, including production systems ownership.
• 4+ years owning ML systems end-to-end in production, including on-call and architecture decisions.
• Strong Kubernetes experience for services, deployments, autoscaling, observability.
• Hands-on with ML lifecycle tooling like MLflow (tracking, registry, workflows).
• Principal-level leadership: technical direction, roadmaps, reliability outcomes (SLOs, incident response).
• Experience evaluating managed ML platforms (Databricks, SageMaker, Vertex AI).

Nice to have

• Databricks workflows and ML tooling.
• Inference and serving frameworks.
• Feature store patterns, model evaluation at scale.
• Optimization systems, decision engines, LLM/agent workflows.

Culture & Benefits

• Remote-first, globally distributed team with hirify.globals Everywhere program (off-sites, local gatherings).
• Healthcare (90% covered, incl. dependents, pets), take-as-much-as-needed vacation, flexible hours.
• One week company-wide winter slowdown, 3 Volunteer Days Off.
• WFH stipend, charity match up to $100, learning stipend, career growth programs.