Machine Learning Engineer

Benefits

Parental leave

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Non-birth-parent leave

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Family-building benefits

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• Adoption assistance: Not verified
• Surrogacy assistance: Not verified

Mental health support

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Relocation assistance

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Childcare support

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Learning budget

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Verification

Not verified checked Jun 13, 2026

Salary

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401(k) match

Reported from DOL Form 5500 industry filing (not employer-specific)

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Market context

U.S. role benchmark (BLS OEWS)

$111,944 U.S. median for this role

Projected growth (BLS Employment Projections)

+13.7% - Much faster than average

Matched to SOC 15-1252 - Data and ML aggregate by role bucket.

Source: U.S. Bureau of Labor Statistics, OEWS, May 2024 and Employment Projections, 2024-2034.

Schedule

Shift type

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Weekend work

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Application

Cover letter

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Assessment

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Deadline

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Where they hire

State eligibility is not yet verified.

About this role

Machine Learning Engineer Work Location (Country) United States | Remote/Onsite Onsite We are seeking an early‑career Machine Learning Engineer who is excited to grow rapidly by building and deploying production‑grade ML systems. The ideal candidate has a strong engineering mindset, has contributed to shipping ML features or products end‑to‑end, and is eager to take ownership across the full lifecycle-from data pipelines to model design to deployment, monitoring, and iteration in real‑world environments. This role offers hands‑on exposure to applied ML, working with IoT datasets, user needs, and product requirements to build scalable solutions that deliver measurable customer ROI. Responsibilities: Design, build, and deploy ML models into production environments, ensuring reliability, scalability, and performance. Ability to select and apply the appropriate ML approach for a given problem - including supervised learning (e.g., logistic regression, random forest, gradient boosting), unsupervised learning (e.g., clustering, dimensionality reduction), and deep learning techniques when appropriate. Develop and maintain feature engineering pipelines, data preprocessing flows, and training workflows. Collaborate with cross‑functional partners including product, data engineering, DevOps & QA to deliver end‑to‑end ML solutions. Work with DevOps team to implement robust MLOps practices, including versioning, CI/CD for ML, monitoring/alerting, automated retraining, and model governance. Continuously evaluate and improve models by monitoring performance, identifying and a

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