Senior Machine Learning Engineer – MLOps

Redwood Logistics

. Design, build, and maintain the ML platform used to train, evaluate, version, and deploy models .

Posted 11/2/2026full-timeRemote • United StatesSenior💰 $140,000 - $190,000 per yearWebsite

Core Competencies

Role fit

Core Competencies

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Demonstrates expertise in building and maintaining ML platforms, including model serving infrastructure and monitoring systems. Proficient in Python engineering and MLOps practices, with a strong focus on the end-to-end ML lifecycle in production environments.

Highest-signal resume keywords

MLOps EngineeringPython EngineeringML Platform ExperienceCI/CD for MLModel Monitoring

ATS Keywords

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Applicant Tracking System Keywords

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Hard Skills

Machine Learning FundamentalsModel Evaluation MetricsData PipelinesETL PipelinesFeature EngineeringModel DeploymentModel RetrainingData Drift DetectionA/B TestingCanary Releases

Soft Skills

Stakeholder CommunicationCollaboration

Tools & Technologies

MLflowSageMakerVertex AIDatabricksAirflowPrefectKubeflowRedshiftSnowflake

Certifications & Qualifications

MS or PhD in Computer ScienceData ScienceApplied MathematicsPhysics

Industry Keywords

Supply ChainLogisticsTransportationFreight PricingDemand Forecasting

Tech Stack

Tools & technologies

AirflowAmazon RedshiftETLPythonSnowflake

About the role

Key responsibilities & impact

Design, build, and maintain the ML platform used to train, evaluate, version, and deploy models
Develop and maintain model-serving infrastructure for real-time low-latency REST APIs and batch large-scale scoring jobs
Implement canary releases, shadow-mode evaluation, A/B testing infrastructure, and rollback mechanisms
Build model monitoring systems to detect data drift, concept drift, prediction distribution shifts, and degradation in business outcomes
Partner with data scientists to understand model requirements, reduce friction to production, and build productivity tooling
Work with Product and Engineering to integrate model predictions into product features and operational workflows
Identify opportunities to drive business impact through data exploration and stakeholder collaboration
Own the feature store, including feature pipelines, feature definitions, training-serving consistency, and reducing duplicate feature engineering
Collaborate with software engineers, data scientists, business stakeholders, and product teams to understand inputs, assumptions, and business processes
Own the ML platform layer across training pipelines, model registries, serving infrastructure, feature stores, and monitoring systems for models used in freight pricing, load matching, demand forecasting, and carrier scoring

Requirements

What you’ll need

MS or PhD in Computer Science, Data Science, Applied Mathematics, Physics, or equivalent work experience
5+ years of software or ML engineering experience
3+ years focused on MLOps, ML platform engineering, or ML infrastructure in a production environment
Proven experience owning the end-to-end ML lifecycle in production: training, deployment, monitoring, and retraining at scale
Strong Python engineering skills; production-grade, tested, maintainable code across ML and data pipelines
Hands-on experience with at least one major ML platform: MLflow, SageMaker, Vertex AI, or Databricks
Experience with a pipeline orchestration tool: Airflow, Prefect, or Kubeflow
Solid 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 working with data warehouses and ETL pipelines, such as Redshift, Databricks, Snowflake, or Airflow
Ability to communicate with technical and non-technical stakeholders is a plus
Three years of experience in supply chain, logistics, transportation, or a similar space is a plus
Must live within the U.S.

Benefits

Comp & perks

Access to experts and resources for your Learning & Development journey
Opportunity for internal mobility
Employee referral bonus program
Employee Resource Groups (ERGs)
Annual fundraising and volunteer events to give back to communities
Paid time off, floating holidays, time off to volunteer and rollover
Paid parental leave
Medical, dental, vision and 401k plans (with match)
Flexible spending account, mass transit and dependent care plans available
Health savings account, with a annual company contribution for plan participants
Short-term and long-term disability; life insurance policies subsidized by company
Pet insurance
Accident care
Access to legal advice
Annual incentives based on individual and company performance
Flexible work schedule based on coverage