Senior Machine Learning Engineer – MLOps

This is a fully remote position, open to applicants in United States.

📋 Description

• Design, develop, and maintain the machine learning platform utilized for training, evaluating, versioning, and deploying models.

• Create and sustain model-serving infrastructure for real-time low-latency REST APIs and large-scale batch scoring tasks.

• Execute canary releases, establish shadow-mode evaluations, develop A/B testing infrastructure, and implement rollback mechanisms.

• Construct model monitoring systems to identify data drift, concept drift, shifts in prediction distributions, and declines in business outcomes.

• Collaborate with data scientists to comprehend model requirements, ease the transition to production, and enhance productivity tools.

• Partner with Product and Engineering teams to incorporate model predictions into product functionalities and operational workflows.

• Discover opportunities to create business impact through data exploration and collaboration with stakeholders.

• Manage the feature store, including feature pipelines, feature definitions, ensuring training-serving consistency, and minimizing redundant feature engineering.

• Work together with software engineers, data scientists, business stakeholders, and product teams to grasp inputs, assumptions, and business processes.

• Lead the ML platform layer encompassing training pipelines, model registries, serving infrastructure, feature stores, and monitoring systems used for freight pricing, load matching, demand forecasting, and carrier scoring.

⛳️ Requirements

• MS or PhD in Computer Science, Data Science, Applied Mathematics, Physics, or equivalent professional experience.

• Over 5 years of experience in software or machine learning engineering.

• At least 3 years dedicated to MLOps, ML platform engineering, or ML infrastructure within a production setting.

• Demonstrated experience managing the complete ML lifecycle in production: training, deployment, monitoring, and retraining at scale.

• Strong Python engineering capabilities; ability to produce production-grade, tested, and maintainable code across ML and data pipelines.

• Practical experience with at least one significant ML platform: MLflow, SageMaker, Vertex AI, or Databricks.

• Familiarity with a pipeline orchestration tool: Airflow, Prefect, or Kubeflow.

• Comprehensive understanding of machine learning fundamentals, model behavior, evaluation metrics, and implications of drift.

• Experience in building CI/CD processes for ML, including automated testing, staging environments, canary deployments, and rollback strategies.

• Background in working with data warehouses and ETL pipelines, such as Redshift, Databricks, Snowflake, or Airflow.

• Excellent communication skills with both technical and non-technical stakeholders is advantageous.

• Three years of experience in supply chain, logistics, transportation, or a related field is a plus.

• Must reside within the U.S.

🏝️ Benefits

• Access to experts and resources to support your Learning & Development journey.

• Opportunities for internal mobility.

• Employee referral bonus program.

• Employee Resource Groups (ERGs).

• Annual fundraising and volunteer events to support communities.

• Paid time off, floating holidays, time off for volunteering, and rollover options.

• Paid parental leave.

• Medical, dental, vision, and 401k plans (with matching contributions).

• Flexible spending accounts, mass transit, and dependent care plans available.

• Health savings account, with annual contributions from the company for plan participants.

• Short-term and long-term disability insurance; life insurance policies subsidized by the company.

• Pet insurance.

• Accident care coverage.

• Access to legal advice.

• Annual incentives based on individual and company performance.

• Flexible work schedule based on coverage needs.