Senior Machine Learning Engineer (MLOps)

Location: Remote within the U.S.; Monday through Friday, 8:00 AM to 5:00 PM with a one-hour break, with flexibility based on coverage.

Salary: $140,000–$190,000 annually, plus eligibility for annual incentives based on individual and company performance.

Company

hirify.global provides supply chain technology, data-centric network solutions, and an integrated logistics platform supporting freight operations across North America and Mexico.

What you will do

• Design, build, and maintain the ML platform used to train, evaluate, version, and deploy models.
• Develop model-serving infrastructure for real-time low-latency REST APIs and large-scale batch inference.
• Implement canary releases, shadow-mode evaluation, A/B testing infrastructure, and rollback mechanisms.
• Build monitoring systems for data drift, concept drift, prediction shifts, and business-outcome degradation.
• Own the feature store, including feature pipelines, definitions, training-serving consistency, and reduced duplication.
• Partner with data scientists, product, engineering, and business stakeholders to integrate predictions into products and operational workflows.

Requirements

• MS or PhD in Computer Science, Data Science, Applied Mathematics, Physics, or equivalent work experience.
• 5+ years of software or ML engineering experience, including 3+ years focused on MLOps, ML platform engineering, or ML infrastructure in production.
• Experience owning the end-to-end production ML lifecycle: training, deployment, monitoring, and retraining at scale.
• Strong Python engineering skills and experience writing production-grade, tested, maintainable code for ML and data pipelines.
• Hands-on experience with at least one major ML platform, such as MLflow, SageMaker, Vertex AI, or Databricks, and one orchestration tool, such as Airflow, Prefect, or Kubeflow.
• Understanding of machine learning fundamentals, model behavior, evaluation metrics, and drift implications.
• Experience building CI/CD for ML, including automated testing, staging environments, canary deployments, and rollback strategies.
• Experience with data warehouses and ETL pipelines, such as Redshift, Databricks, Snowflake, or Airflow.

Nice to have

• Ability to communicate with technical and non-technical stakeholders.
• Three years of experience in supply chain, logistics, transportation, or a similar field.

Culture & Benefits

• Access to learning and development resources, internal mobility, employee resource groups, and referral bonuses.
• Paid time off, floating holidays, volunteer time off, rollover options, and paid parental leave.
• Medical, dental, vision, and 401(k) plans with matching contributions.
• Flexible spending, health savings, mass transit, and dependent care plans.
• Short- and long-term disability coverage, subsidized life insurance, pet insurance, accident care, and legal advice.
• Annual fundraising and volunteer events supporting local communities.