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Principal Machine Learning Jobs (NOW HIRING)

Ibotta, Inc

Principal Machine Learning Engineer

Denver, CO · On-site

$228K - $253K/yr

Ibotta is seeking a Principal Machine Learning Engineer to join our Core Data & Analytics team and contribute to our mission to Make Every Purchase Rewarding. We're looking for someone who has a ...

Gather AI

Principal Machine Learning Scientist

About the Role We are looking for a Principal Machine Learning Scientist to advance the state of our computer vision systems for warehouse inventory scanning. You will work across the full ML ...

BigHat

Principal, Machine Learning Scientist

San Mateo, CA · On-site

$254K - $290K/yr

Principal, Machine Learning Scientist Department: DS/ML (Data Science/Machine Learning) Employment Type: Full Time Location: San Mateo, CA Reporting To: Hunter Elliot Description The role: We are ...

Appgate

Principal Machine Learning Engineer

Manhattan, NY · On-site

$220K - $265K/yr

About the Role We are seeking an exceptional Principal Machine Learning Engineer to lead the design and development of the next generation of our AI-driven fraud detection platform . You will ...

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How much do principal machine learning jobs pay per year?

As of Sep 10, 2026, the average yearly pay for principal machine learning in the United States is $109,393.00, according to ZipRecruiter salary data. Most workers in this role earn between $85,000.00 and $125,000.00 per year, depending on experience, location, and employer.

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Principal Machine Learning Engineer

Philadelphia, PA • On-site

Apetan Consulting llc
IT Services • 1 - 10 employees
Learn more about Apetan Consulting llcexternal

$80 - $150/hr

Contractor

Re-posted 24 days ago

Job description

Title: Principal Machine Learning Engineer

Duration: 6 Mos C2H (without sponsorship)

Location: Hybrid in Philadelphia, PA onsite Tue & Wed each week (Local candidates preferred but, those willing to relocate are acceptable)

Principal Machine Learning Engineer to serve as a hands-on technical leader for machine learning, predictive modeling, scoring, decisioning, and applied AI initiatives. This role will primarily focus on building, validating, deploying, and improving machine learning models, while also bringing principal-level judgment to problem definition, model design, stakeholder engagement, and production readiness.

Hands-On Model Development

  • Build, test, validate, and improve machine learning models for scoring, prediction, prioritization, risk detection, engagement, intervention targeting, and decision support.
  • Perform exploratory data analysis, data quality assessment, feature engineering, model training, model selection, and performance evaluation.
  • Develop practical ML models that balance predictive performance, explainability, stability, maintainability, and business usefulness.
  • Work with structured, semi-structured, and operational data to create model-ready datasets and reusable features.
  • Use tools such as Python, SQL, Spark, Databricks, MLflow, scikit-learn, XGBoost, or similar platforms and libraries.
  • Move quickly from data exploration to prototype to validated model to production-ready capability.

 

Required Qualifications

  • Professional experience in machine learning, data science, software engineering, analytics engineering, applied AI, or related technical fields.
  • 5+ years of hands-on machine learning model development experience, including feature engineering, model training, validation, evaluation, and iteration.
  • 3+ years of experience deploying, operationalizing, or supporting models in production or business-critical environments.
  • Strong hands-on experience with Python and SQL.
  • Experience with modern ML and data platforms such as Databricks, Spark, MLflow, Snowflake, Azure, AWS, or similar technologies.
  • Strong understanding of model evaluation, calibration, thresholding, score interpretation, monitoring, drift, retraining, and production ML lifecycle management.
  • Experience translating ambiguous business problems into concrete ML designs, model requirements, validation plans, and measurable outcomes.
  • Ability to explain model behavior, model performance, assumptions, limitations, and tradeoffs to both technical and non-technical stakeholders.
  • Strong engineering discipline, including clean code, reproducibility, versioning, testing, documentation, and maintainability.
  • Ability to work independently as a senior hands-on contributor while also providing technical leadership and modeling judgment.

Scoring, Scorecards, and Transparent Models

Production ML and MLOps

Product and Rapid-Build Execution

Generative AI and AI Automation

Requirement Shaping and Stakeholder Partnership