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Interpretability Ai Jobs (NOW HIRING)

Goodfire

Machine Learning Engineer

Manhattan, NY · 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 ...

Goodfire

Machine Learning Engineer

San Francisco, CA · On-site

Goodfire is a research company focused on advancing the science of AI interpretability. They are seeking Machine Learning Engineers to build their platform for training, evaluating, and deploying ...

Goodfire

Research Scientist

San Francisco, CA · On-site

Goodfire is a research company focused on AI interpretability, aiming to build safe and powerful AI systems. The Research Scientist role involves developing new techniques for understanding and ...

Goodfire

Product Marketing Lead

San Francisco, CA · On-site

About Goodfire Goodfire is a research company using interpretability to understand, learn from, and design AI systems. Our mission is to build the next generation of safe and powerful AI-not by ...

Goodfire

Machine Learning Engineer

San Francisco, CA · On-site

Goodfire is an AI research lab using interpretability to turn AI into something that can be understood, debugged, and shaped like software Founded in 2024, the company is headquartered in San ...

Spear AI

Create governance processes for AI model lifecycle management * Implement model interpretability and explainability solutions * Establish metrics and monitoring systems for RAI compliance * Lead ...

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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.

Machine Learning Engineer

Goodfire

Manhattan, NY • On-site

Full-time

Re-posted 6 days ago

Job description

Job Summary:
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, evaluating, and deploying interpretable AI systems at scale, contributing to various aspects such as interpretability tools and training infrastructure.
Responsibilities:
• Turn cutting edge interpretability research into production ready tools.
• Optimize pipelines and infrastructure for frontier model interpretability, training, and inference.
• Integrate new machine learning workflows and pipelines into our product and deploy to customers.
• Ensure system reliability, reproducibility, and performance.
Qualifications:
Required:
• 5+ years of experience in ML infra, research engineering, or systems programming.
• Comfort working across research and engineering boundaries.
• Expertise in Python, PyTorch or Jax, and distributed systems.
• Experience deploying and maintaining ML systems at scale.
• You care about understanding how models work internally and using that to make them more reliable and useful in the real world.
Preferred:
• Open-source ML infra contributions.
• Startup or frontier lab experience in fast-moving teams.
Company:
Goodfire is an AI research lab using interpretability to turn AI into something that can be understood, debugged, and shaped like software Founded in 2024, the company is headquartered in San Francisco, USA, with a team of 11-50 employees. The company is currently Early Stage.

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