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Principal Machine Learning Engineer 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 ...

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 ...

HubSpot

Principal Machine Learning Engineer- AI Context

$138K - $185K/yr

POS-22209 Principal Machine Learning Engineer HubSpot is an all-in-one marketing, sales, and service software platform that helps businesses grow and succeed. With a user-friendly interface and ...

Adobe

Principal Machine Learning Engineer

Seattle, WA · On-site

We are hiring a Principal Machine Learning Engineer to serve as the technical lead for our GenAI Services area. This is not a model-training or research role - it is the senior-most hands-on ...

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How much do principal machine learning engineer jobs pay per year?

As of Sep 10, 2026, the average yearly pay for principal machine learning engineer in the United States is $147,220.00, according to ZipRecruiter salary data. Most workers in this role earn between $118,500.00 and $173,000.00 per year, depending on experience, location, and employer.

What does a principal machine learning engineer do?

A Principal Machine Learning Engineer leads the design, development, and deployment of machine learning models and systems. They set technical strategy, mentor engineers, and collaborate with cross-functional teams to solve complex AI challenges. Their role often includes researching new algorithms, optimizing model performance, and ensuring scalability in production environments. Additionally, they work closely with data scientists, software engineers, and product managers to align ML initiatives with business objectives.

What skills and qualifications are needed to thrive as a principal machine learning engineer?

To thrive as a Principal Machine Learning Engineer, you need advanced expertise in machine learning algorithms, statistical analysis, software engineering, and a strong background in computer science or related fields, often supported by a master's or PhD degree. Familiarity with tools such as Python, TensorFlow, PyTorch, cloud platforms (AWS, GCP, Azure), and relevant certifications strengthens technical capability. Leadership, strategic thinking, effective communication, and mentorship are vital soft skills for guiding teams and collaborating across departments. These competencies are essential for driving innovation, ensuring technical excellence, and influencing organizational AI initiatives.

What types of projects and responsibilities can a principal machine learning engineer typically expect?

Principal Machine Learning Engineers are often tasked with leading the design, development, and deployment of large-scale machine learning models and systems that address key business challenges. In this role, you will collaborate closely with data scientists, engineers, and product managers to define project requirements, architect solutions, and ensure high-quality delivery. You may also guide research initiatives, oversee code and model reviews, and mentor junior engineers, helping to shape the technical direction of the team. Typical responsibilities can range from prototyping and optimizing algorithms to ensuring models are scalable, reliable, and aligned with organizational goals.

Is a principal machine learning engineer a high paying job?

A principal machine learning engineer is typically a high-level role with a competitive salary that reflects advanced expertise in machine learning, data science, and software engineering. Salaries often vary based on industry, location, and company size but generally exceed those of mid-level roles due to the experience and leadership responsibilities involved.
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Principal Machine Learning Engineer jobs near you
Infographic showing various Principal Machine Learning Engineer job openings in the United States as of September 2026, with employment types broken down into 1% Internship, 1% As Needed, 74% Full Time, 23% Part Time, and 1% Contract. Highlights an 83% Physical, 2% Hybrid, and 15% Remote job distribution, with an average salary of $147,220 per year, or $70.8 per hour.

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 25 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