Senior Machine Learning Engineer – MLOps

Job not on LinkedIn

🔥 4 minutes ago

🇺🇸 United States – Remote

💵 $140k - $190k / year

⏰ Full Time

🟠 Senior

🤖 Machine Learning Engineer

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Redwood Logistics

WebsiteLinkedInAll Job Openings

1001 - 5000 employees

Founded 2001

🚗 Transport

📦 Logistics

Transport • Logistics • Supply Chain

Redwood Logistics is a next-generation leader in fourth-party logistics (4PL), dedicated to modernizing supply chains through innovative technology and comprehensive logistics management. Utilizing their Logistics Platform as a Service (LPaaS), Redwood offers flexible, end-to-end supply chain solutions that integrate various services and technologies, providing customers with custom-tailored logistics strategies. With an emphasis on automation and visibility, Redwood empowers businesses of all sizes to optimize their supply chain operations, ensuring efficient and headache-free management of freight and logistics processes.

📋 Description

• 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

• 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

• 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

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