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Interpretability Ai Jobs (NOW HIRING)

Goodfire

Machine Learning Engineer

San Francisco, CA · On-site

Goodfire is a research company focused on building safe and powerful AI systems through interpretability. They are seeking Machine Learning Engineers to develop their platform for training ...

ClifyX

Prinicipal AI Engineer

West Des Moines, IA · On-site

... reliability, interpretability, and governance. • Architect robust MLOps pipelines--CI/CD, model versioning, drift detection, automated retraining, monitoring. • Lead the development of AI ...

FAR.AI

$170K - $270K/yr

... AI, from demonstrating superhuman systems can be vulnerable, to scaling laws for robustness and jailbreaking constitutional classifiers. Mechanistic Interpretability: finding issues with Sparse ...

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Interpretability Ai information

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$44.5K

$129.7K

$177.5K

How much do interpretability ai jobs pay per year?

As of Aug 11, 2026, the average yearly pay for interpretability ai in the United States is $129,716.00, according to ZipRecruiter salary data. Most workers in this role earn between $114,500.00 and $137,500.00 per year, depending on experience, location, and employer.

What is interpretability in AI?

Interpretability in AI refers to the ability to understand and explain how artificial intelligence systems, especially complex models like neural networks, make their decisions. It helps researchers, developers, and end-users to trust AI systems by making their inner workings more transparent. Interpretability is crucial in sensitive fields such as healthcare and finance, where decisions need to be justified and understood. Techniques for interpretability include feature importance, visualization, and model simplification. Improving interpretability can lead to safer, fairer, and more accountable AI systems.

What is the difference between Interpretability Ai vs Data Scientist?

AspectInterpretability AiData Scientist
Required CredentialsTypically a background in AI, machine learning, or data analysis; often a master's or PhD in related fieldsDegree in computer science, statistics, or related fields; often a master's or PhD
Work EnvironmentResearch labs, AI development teams, tech companies focusing on explainable AIData analysis, modeling, and insights generation across various industries
Employer & Industry UsageTech firms, AI startups, research institutionsFinance, healthcare, tech, consulting, and more

Interpretability Ai specialists focus on making AI models transparent and understandable, often working on explainability tools. Data Scientists analyze data, build models, and generate insights. While both roles require strong analytical skills, Interpretability Ai emphasizes explainability techniques, whereas Data Scientists focus on data analysis and modeling across diverse industries.

What are the key skills and qualifications needed to thrive as an AI interpretability specialist, and why are they important?

To thrive as an AI Interpretability Specialist, you need expertise in machine learning, statistics, and data analysis, often backed by a degree in computer science, mathematics, or a related field. Familiarity with interpretability frameworks (like LIME, SHAP), deep learning libraries (such as TensorFlow or PyTorch), and experience with model evaluation tools are typically required. Strong problem-solving abilities, communication skills, and intellectual curiosity help bridge the gap between technical results and stakeholder understanding. These competencies are essential to ensure AI models are transparent, trustworthy, and aligned with ethical standards.

What are the main challenges faced when working in interpretability AI roles, and how can professionals address them?

Professionals in Interpretability AI often face the challenge of translating complex machine learning models into understandable insights for both technical and non-technical stakeholders. This requires not only a deep understanding of algorithms but also strong communication skills to bridge the gap between data scientists, engineers, and decision-makers. Additionally, balancing the trade-off between model accuracy and interpretability can be tricky, as more interpretable models may sometimes be less accurate. Collaborating closely with cross-functional teams and staying updated with the latest interpretability techniques can help overcome these challenges and add value to AI projects.
More about Interpretability Ai jobs
What cities are hiring for Interpretability Ai jobs? Cities with the most Interpretability Ai job openings:
What states have the most Interpretability Ai jobs? States with the most job openings for Interpretability Ai jobs include:
What job categories do people searching Interpretability Ai jobs look for? The top searched job categories for Interpretability Ai jobs are:
Interpretability Ai jobs near you
Infographic showing various Interpretability Ai job openings in the United States as of August 2026, with employment types broken down into 100% Full Time. Highlights an 100% In-person job distribution, with an average salary of $129,716 per year, or $62.4 per hour.

Member of the Technical Staff, Interpretability

Output, Inc

New York, NY • On-site

$150K - $350K/yr

Full-time

Medical, Dental, Vision

Re-posted 7 days ago

Job description

Output has built a biological reasoning model that understands biology at the scale and complexity life actually operates. Our model independently learned the principles of molecular interactions, opening up drug treatments that were previously impossible. We're already generating therapies that traditional approaches cannot reach. The hardest problems in both AI and biology are being solved here, and there is room for you to own one.
Output is currently in stealth, operated by a team of repeat founders and biotech veterans with multiple exits in AI x Bio, and backed by top-tier VCs including Y Combinator.
You will continue developing methods to understand what our foundation model learns about biology, and build the tools that make it a glass box model. We believe that in biology, a model's reasoning must be visible. And the features you find are not just explanations: they expand what the model can do.
  • You will continue developing our methods for probing and reverse-engineering the model's learned representations, understanding how it encodes biological information across molecular scales
  • You will design and run experiments to identify and characterize capabilities, mapping what the model has learned about molecular interactions and biological function
  • You will build methods to extract the model's biological understanding as explicit, usable outputs that downstream systems and researchers can act on
  • You will create tools that connect model internals to meaningful biological concepts, making the model's reasoning interpretable to scientists and useful in practice
  • You will work closely with the pretraining and generation teams, feeding interpretability findings back into model development to strengthen the capabilities you uncover
  • You will own the full pipeline from probing experiments to production-quality interpretability tools, building robust systems on distributed infrastructure

About You
  • You have a PhD in computer science, machine learning, physics, mathematics, or a related field with 2+ years of post-doctoral or industry research experience, or a Bachelor's or Master's degree with 5+ years of hands-on research and engineering experience in model interpretability or representation analysis
  • You have a strong publication record at top-tier venues (e.g., NeurIPS, ICML, ICLR) with contributions to mechanistic interpretability, representation analysis, probing methods, or model understanding
  • You have hands-on experience analyzing the internal representations of large neural networks, with demonstrated ability to design experiments that reveal what models have learned
  • You are proficient in Python and PyTorch, and have experience working with large models on GPU infrastructure
  • You have demonstrated the ability to take interpretability research from experiments to usable tools: you do not just analyze models, you build systems others can use
  • You write production-quality code that is well-tested and maintainable, and you are comfortable working in shared codebases with version control and code review
  • You think carefully about what constitutes evidence that a model has learned a concept, and you design experiments that distinguish real capabilities from artifacts

Bonus Points
  • You have a background in chemistry, biology, computational biology, biophysics, or a related natural science
  • You have experience interpreting ML models trained on scientific or biological data
  • You have experience building visualization or analysis tools for model internals
  • You have experience with multimodal models or representations that span multiple data types
  • You have contributed to open-source machine learning projects

Our Values
♥ Heart: We foster a culture of ownership. We are assembling a team of individuals who are passionate and take pride in their contributions.
Excellence: We have an unwavering commitment to excellence and continuously challenge ourselves to reach the highest standards.
Practicality: We value practicality and results-oriented thinking. We are committed to making a tangible impact on the lives of patients and the broader community.
Honesty: We place a high value on honesty and directness. We firmly believe in addressing issues as they arise, in an open and transparent manner.
Fun: We believe that life is too short to not have fun. Our goal is to create a workplace that is fun, engaging, rewarding and fulfilling.
What We Offer
  • We encourage new and different ideas, creativity and contrarian thinking
  • Healthy feedback focused environment to help you strive - leadership will have high expectations, regularly share constructive feedback, support you and help you grow, and welcome receiving feedback and ideas from you
  • You own your day-to-day management. What we care about is that we all hit our milestones
  • Competitive salary and equity in a growing, well-funded startup
  • Excellent medical, dental, and vision coverage

Apply