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Interpretable Machine Learning Jobs (NOW HIRING)

AcuityMD

OR · On-site

$122.40K - $161.30K/yr

Document models, assumptions, and data contracts so results are interpretable and reproducible for internal and external audiences Your Profile * You have 6+ years of experience in machine learning ...

AcuityMD

$133.10K - $175.50K/yr

Document models, assumptions, and data contracts so results are interpretable and reproducible for internal and external audiences Your Profile * You have 6+ years of experience in machine learning ...

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Interpretable Machine Learning information

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$25.5K

$42.6K

$88K

How much do interpretable machine learning jobs pay per year?

As of Jun 4, 2026, the average yearly pay for interpretable machine learning in the United States is $42,584.00, according to ZipRecruiter salary data. Most workers in this role earn between $32,500.00 and $46,000.00 per year, depending on experience, location, and employer.

What are the key skills and qualifications needed to thrive as an Interpretable Machine Learning Specialist, and why are they important?

To thrive as an Interpretable Machine Learning Specialist, you need a strong background in machine learning concepts, statistical analysis, and a relevant degree in computer science, statistics, or a related field. Familiarity with programming languages such as Python or R, experience using libraries like scikit-learn, SHAP, or LIME, and sometimes certifications in data science are commonly required. Exceptional communication, problem-solving, and critical thinking skills help translate complex models into understandable insights for stakeholders. These skills are crucial to ensure that machine learning models are transparent, trustworthy, and actionable in real-world decision-making.

What are some typical challenges faced when working in an Interpretable Machine Learning role?

Professionals in Interpretable Machine Learning often encounter the challenge of balancing model performance with transparency. It can be difficult to make highly accurate models, such as deep neural networks, understandable to non-technical stakeholders without sacrificing predictive power. Additionally, collaborating closely with domain experts and end users to ensure interpretability methods are both meaningful and actionable is a key part of the role. Regular communication across data science, engineering, and business teams is crucial to ensure that model interpretations align with organizational goals and regulatory requirements.

What is interpretable machine learning?

Interpretable machine learning refers to models and techniques that make it easier for humans to understand how and why a machine learning system makes its predictions or decisions. Unlike traditional 'black box' models, interpretable models provide transparency, enabling users to trace the reasoning behind outputs. This is especially important in fields like healthcare, finance, and law, where understanding model decisions is crucial for trust, accountability, and regulatory compliance. Methods for interpretability include using simpler models (like decision trees), feature importance scores, and visualization techniques. The goal is to balance predictive performance with the ability to explain results to stakeholders.

What is the difference between Interpretable Machine Learning vs Data Scientist?

AspectInterpretable Machine LearningData Scientist
CredentialsKnowledge of ML algorithms, statistics, and interpretability techniquesDegree in data science, statistics, or related field; programming skills
Work EnvironmentFocus on model transparency, explainability, and ethical AIData analysis, model development, and business insights
Industry UsageHealthcare, finance, and regulated sectors requiring explainabilityWide industry application including tech, finance, healthcare

Interpretable Machine Learning specialists focus on creating models that are transparent and explainable, ensuring stakeholders understand how decisions are made. Data Scientists develop a broad range of models and analyze data to generate insights, often prioritizing accuracy over interpretability. While both roles require strong analytical skills, Interpretable Machine Learning emphasizes model transparency, making it essential in regulated industries.

Infographic showing various Interpretable Machine Learning job openings in the United States as of May 2026, with employment types broken down into 94% Part Time, 2% Temporary, and 4% Contract. Highlights an 94% Physical, 1% Hybrid, and 5% Remote job distribution, with an average salary of $42,584 per year, or $20.5 per hour.
Principal Machine Learning Engineer

Principal Machine Learning Engineer

Medical Guardian

Philadelphia, PA • On-site

Full-time

Medical, Dental, Vision, Life, Retirement, PTO

Posted 14 days ago

Job description

About Medical Guardian:
Founded in 2005, Medical Guardian is a fast-growing digital health and safety company on a mission to help people live a life without limits. With 13 consecutive years on the Inc. 5000 list of Fastest Growing Companies, we're redefining what it means to age confidently and independently.
We support over 625,000 members nationwide with life-saving emergency response systems and remote patient monitoring solutions. Trusted by families, healthcare providers, and care managers, our work is powered by a culture of innovation, compassion, and purpose.
Medical Guardian boasts a 95% customer satisfaction rate, a #1 ranking on 16 medical alert consumer choice sites and achieves a 4.7+ star rating on Google Reviews.
Position Overview:
We are looking for a 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.
This is a hands-on model-building role first. The ideal candidate should be comfortable spending most of their time working directly with data, features, models, scoring logic, validation methods, production workflows, and model improvement. They should also be able to operate with the maturity of a principal-level engineer: shaping unclear problems, making pragmatic technical decisions, mentoring others, and driving work forward without waiting for perfect requirements.
Key Responsibilities:
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.

Scoring, Scorecards, and Transparent Models
  • Design and implement predictive scores, risk tiers, score bands, thresholds, cut points, and intervention logic.
  • Build transparent and interpretable models where explainability is important, including logistic regression, generalized linear models, decision trees, monotonic models, calibrated models, scorecard-style models, or explainable boosting approaches.
  • Evaluate models for accuracy, calibration, stability, drift, fairness, interpretability, and operational usefulness.
  • Help stakeholders understand what a score represents, how it should be used, how it should not be used, and how changes in the score should be interpreted.
  • Document model logic, features, assumptions, limitations, validation results, and recommended usage in a way that business and technical stakeholders can understand.
  • Define the evidence needed to show that a model or score is valid, stable, explainable, actionable, and useful.

Production ML and MLOps
  • Partner with data engineering, analytics engineering, platform engineering, and application engineering teams to move models from experimentation into reliable production workflows.
  • Support model deployment, batch scoring, real-time or near-real-time inference, model versioning, monitoring, retraining, and performance tracking.
  • Help define data pipelines, feature pipelines, inference flows, model outputs, feedback loops, and monitoring requirements.
  • Ensure models are observable, supportable, secure, scalable, and aligned with enterprise architecture and governance expectations.
  • Establish practical monitoring and feedback loops to determine whether models continue to perform and create value over time.

Product and Rapid-Build Execution
  • Operate effectively in a rapid-build, startup-like environment where speed, ownership, and pragmatic decision-making are important.
  • Turn early-stage ideas, ambiguous business needs, and rough concepts into working ML products, scores, prototypes, and production capabilities.
  • Bring a product-engineering mindset to ML development, including user needs, workflow integration, adoption, usability, feedback loops, and measurable outcomes.
  • Drive work forward without waiting for perfect requirements, while still identifying critical assumptions, risks, dependencies, and evidence needed before scaling.
  • Partner with business and product stakeholders to define MVPs, iterate quickly, learn from usage, and improve models over time.
  • Make smart tradeoffs between quick prototypes, durable platforms, transparent models, GenAI-enabled workflows, and longer-term ML architecture.

Generative AI and AI Automation
  • Support the design and development of GenAI-enabled solutions, including LLM-powered workflows, RAG, summarization, conversational agents, document intelligence, and decision-support tools.
  • Help evaluate when GenAI is appropriate versus when traditional ML, rules, analytics, or transparent scoring models are a better fit.
  • Partner with product, engineering, and business stakeholders to integrate predictive models, scores, and GenAI outputs into practical workflows.
  • Apply appropriate evaluation, guardrails, monitoring, privacy controls, and human-in-the-loop processes for GenAI use cases.
  • Help the organization balance innovation with explainability, safety, reliability, privacy, and operational usefulness.

Requirement Shaping and Stakeholder Partnership
  • Work directly with business, product, analytics, operations, and engineering stakeholders to clarify what a model is intended to predict, explain, recommend, or trigger.
  • Translate business questions into measurable ML objectives, target variables, features, validation approaches, and success metrics.
  • Ask practical questions early: who will use the score, what action will it inform, what does a false positive or false negative mean, and how will we know the model is creating value?
  • Communicate model behavior, tradeoffs, limitations, and recommended usage clearly to both technical and non-technical audiences.
  • Help the team avoid becoming an AI ticket factory by shaping solutions, not just executing requests.

Principal-Level Technical Leadership
  • Provide technical leadership through hands-on example, strong engineering judgment, and clear recommendations.
  • Proactively identify model risks, data gaps, unclear requirements, design issues, and opportunities for improvement.
  • Help establish practical standards for model development, validation, documentation, monitoring, and production readiness.
  • Mentor other engineers and data scientists through code reviews, design reviews, modeling guidance, and shared best practices.
  • Demonstrate high ownership by driving clarity, execution, and continuous improvement.

Required Qualifications:
  • 8+ years of 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.

Preferred Qualifications:
  • 10+ years of relevant professional experience in ML, data science, applied AI, software engineering, decisioning systems, commercial software, or production analytics.
  • Experience building scorecards, risk scores, health scores, engagement scores, churn scores, fraud scores, credit-style models, prioritization models, or operational decision-support models.
  • Experience with transparent or interpretable models such as logistic regression, GLMs, GAMs, decision trees, monotonic models, calibrated models, scorecard-based models, or Explainable Boosting Machines.
  • Experience designing score bands, thresholds, risk tiers, intervention rules, recommended actions, or decision logic based on model outputs.
  • Experience working in commercial software, SaaS, digital products, gaming, fintech, healthtech, consumer technology, marketplace, or other product-driven environments.
  • Experience building ML, AI, analytics, or decisioning capabilities embedded into customer-facing products, operational workflows, commercial platforms, or revenue-impacting systems.
  • Experience in startup, scale-up, innovation lab, new product development, or rapid-build environments where the candidate had to operate with ambiguity and drive work forward independently.
  • Experience partnering with product managers, designers, software engineers, business leaders, and operational teams to turn ML models into usable product capabilities.
  • Experience building MVPs, validating assumptions, iterating based on feedback, and maturing prototypes into production systems.
  • Experience with GenAI, LLMs, RAG, AI agents, prompt engineering, model evaluation, conversational AI, summarization, document intelligence, or AI-enabled workflow automation.
  • Experience combining traditional ML models with GenAI-enabled workflows, such as using predictive scores to trigger outreach, summarize customer/member context, recommend next actions, or support human decision-making.
  • Experience in healthcare, population health, remote patient monitoring, insurance, financial services, safety, operations, or other domains where model trust and explainability are important.
  • Experience with MLOps practices including model registries, deployment pipelines, monitoring, drift detection, retraining strategies, and model governance.

Success in This Role Looks Like:
  • High-quality models and scores are built, validated, deployed, monitored, and improved over time.
  • Model outputs are explainable and trusted by business and operational stakeholders.
  • Scores are connected to real decisions, workflows, interventions, or measurable outcomes.
  • The organization moves faster because this person can turn ambiguity into working ML capabilities.
  • The ML team has stronger standards for model development, validation, documentation, monitoring, and production readiness.
  • Business partners understand what the models do, how to use them, where their limitations are, and how to interpret changes in outputs.
  • The team avoids building models in isolation and instead builds ML capabilities that are connected to products, workflows, users, and business value.
  • GenAI is applied thoughtfully where it improves workflow, decision support, summarization, automation, or user experience, without replacing appropriate model governance or human judgment.

Benefits
  • Health Care Plan (Medical, Dental & Vision)
  • Paid Time Off (Vacation, Sick Time Off & Holidays)
  • Company Paid Short Term Disability and Life Insurance
  • Retirement Plan (401k) with Company Match

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