Senior Machine Learning Engineer (MLOps)

Reports To: Senior Director of Data Science Location: Remote (within the U.S.) Environment: Remote Status: Exempt, Salaried Who We Are Recognized by Gartner in their Modern 4PL Market Guide, Redwood Logistics is at the forefront of industry innovation. Our cutting-edge supply chain technology pairs with the expertise of our brilliant minds to empower logistics execution across North America and Mexico. Leveraging a comprehensive range of services, data-centric network solutions, and a seamlessly integrated platform, we have established our prominence as a key player in the mid-market segment within the freight tech industry. Whether you’re just starting your career or are an established professional looking for your next opportunity, Redwood inspires innovation across teams to provide transformative solutions for our customers. Purpose Of Your Work Redwood Logistics is seeking a Senior Machine Learning Engineer to build and operate the infrastructure that takes ML models from development to production and keeps them performing once they're there. This role is the connective tissue between data science and engineering: you ensure that models built by the data science team are deployed reliably, monitored continuously, retrained systematically, and governed with rigor. You will own the ML platform layer training pipelines, model registries, serving infrastructure, feature stores, and monitoring systems across a portfolio of models driving freight pricing, load matching, demand forecasting, carrier scoring, and more. This is a deeply technical role that requires equal parts ML knowledge and software engineering discipline. How You Make a Difference Everyday Design, build, and maintain the ML platform data scientists and ML engineers use to train, evaluate, version, and deploy models across the organization. Develop and maintain model serving infrastructure supporting both real-time (low-latency REST APIs) and batch (large-scale scoring jobs) inference patterns. Implement safe deployment patterns: canary releases, shadow mode evaluation, A/B testing infrastructure, and rollback mechanisms for production models. Build comprehensive model monitoring systems that detect data drift, concept drift, prediction distribution shifts, and degradation in business outcomes. Partner closely with data scientists to understand model requirements, reduce friction to production, and build tooling that gives them leverage. Work with Product and Engineering to integrate model predictions into product features and operational workflows reliably and at scale. Identify new opportunities to drive business impact through data exploration and collaboration with key stakeholders. Own the feature store: design feature pipelines, manage feature definitions, ensure consistency between training and serving environments, and reduce feature engineering duplication across teams. Collaborate with cross-functional teams, including software engineers, data scientists, business stakeholders, and product teams, to gather inputs and assumptions and understand business processes along the way. You’ve Got This? MS or PhD in Computer Science, Data Science, Applied Mathematics, Physics, or equivalent work experience. 5+ years of software or ML engineering experience, with 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, Databricks) and pipeline orchestration tool (Airflow, Prefect, Kubeflow). Solid understanding of machine learning fundamentals, enough to reason about model behavior, evaluation metrics, and the implications of drift. Experience building CI/CD for ML: automated testing, staging environments, canary deployments, and rollback strategies for models. Experience working with Data Warehouses and ETL pipelines (e.g., Redshift, Databricks, Snowflake, 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.

What We Offer

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 Additional benefits including pet insurance, accident care, access to legal advice and more Work Schedule This position is full-time and remote Monday through Friday from 8:00 AM to 5:00 PM with an hour break, but flexibility is available based on coverage.

Compensation

Range Salary Range $140,000 - $190,000 This position is eligible to earn annual incentives based on individual and company performance. The estimated pay range reflects an anticipated range for this position. The actual base salary offered will depend on a variety of factors, including the qualifications of the individual applicant for the position, years of relevant experience, specific and unique skills, level of education attained, certifications or other professional licenses held, and the geographical location in which the applicant lives and/or which they will be performing the job. Redwood is an equal opportunity employer. Employment decisions at the Company are based on individual merit, qualifications, abilities, and the Company’s needs and resources. The Company does not discriminate in recruiting, hiring, compensation, promotions, discipline, termination or any other aspect of employment on the basis of an individual’s actual or perceived race, color, creed, religion, sex (including pregnancy, childbirth and related medical conditions), sexual orientation, gender identity, national origin, ancestry, citizenship status, age, disability, marital status, military service or status, genetic information, arrest and conviction record, credit history, or any other basis protected by applicable law.