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Interpretability Ai Jobs in Ladera Ranch, CA (NOW HIRING)

FieldAI

Agentic AI/ML Engineer, Multimodal

Irvine, CA · On-site

... interpretability. • Build scalable evaluation pipelines for vision and multimodal models. • Contribute to model observability, drift detection, and error classification. • Fine-tune and ...

FieldAI

Agentic AI/ML Engineer, Multimodal

Irvine, CA

Curate datasets and develop tools to improve model interpretability . * Build scalable evaluation ... D. in Computer Science, AI/ML, Robotics, or equivalent industry experience. * 2+ years of industry ...

FieldAI

Agentic AI/ML Engineer, Multimodal

Irvine, CA · On-site

Curate datasets and develop tools to improve model interpretability . * Build scalable evaluation ... D. in Computer Science, AI/ML, Robotics, or equivalent industry experience. * 2+ years of industry ...

FieldAI

Agentic AI/ML Engineer, Multimodal

Irvine, CA · On-site

Curate datasets and develop tools to improve model interpretability . * Build scalable evaluation ... D. in Computer Science, AI/ML, Robotics, or equivalent industry experience. * 2+ years of industry ...

All JobsInterpretability Ai JobsInterpretability Ai Jobs in Ladera Ranch, CA

Interpretability Ai information

See Ladera Ranch, CA salary details

$47.3K

$137.9K

$188.7K

How much do interpretability ai jobs pay per year?

As of Sep 5, 2026, the average yearly pay for interpretability ai in Ladera Ranch, CA is $137,893.00, according to ZipRecruiter salary data. Most workers in this role earn between $121,700.00 and $146,200.00 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 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.

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 cities near Ladera Ranch, CA are hiring for Interpretability Ai jobs?

Cities near Ladera Ranch, CA with the most Interpretability Ai job openings:

Interpretability Ai jobs near you

Agentic AI/ML Engineer, Multimodal

FieldAI

Irvine, CA • On-site

Full-time

Re-posted 18 days ago

Job description

Job Summary:
FieldAI is transforming how robots interact with the real world by building risk-aware, reliable, and field-ready AI systems. As an AI/ML Engineer on the FiFM team, you will drive research and model development focused on multimodal data, computer vision, and agentic AI, contributing to the company's innovative initiatives in robotics.
Responsibilities:
• Train and fine-tune million- to billion-parameter multimodal models, with a focus on computer vision, video understanding, and vision-language integration.
• Track state-of-the-art research, adapt novel algorithms, and integrate them into FiFM.
• Curate datasets and develop tools to improve model interpretability.
• Build scalable evaluation pipelines for vision and multimodal models.
• Contribute to model observability, drift detection, and error classification.
• Fine-tune and optimize open-source VLMs and multimodal embedding models for efficiency and robustness.
• Build and optimize Multi-VectorRAG pipelines with vector DBs and knowledge graphs.
• Create embedding-based memory and retrieval chains with token-efficient chunking strategies.
Qualifications:
Required:
• Master’s/Ph.D. in Computer Science, AI/ML, Robotics, or equivalent industry experience.
• 2+ years of industry experience or relevant publications in CV/ML/AI.
• Strong expertise in computer vision, video understanding, temporal modeling, and VLMs.
• Proficiency in Python and PyTorch with production-level coding skills.
• Experience building pipelines for large-scale video/image datasets.
• Familiarity with AWS or other cloud platforms for ML training and deployment.
• Understanding of MLOps best practices (CI/CD, experiment tracking).
• Hands-on experience fine-tuning open-source multimodal models using HuggingFace, DeepSpeed, vLLM, FSDP, LoRA/QLoRA.
• Knowledge of precision tradeoffs (FP16, bfloat16, quantization) and multi-GPU optimization.
• Ability to design scalable evaluation pipelines for vision/VLMs and agent performance.
Preferred:
• Experience with Agentic/RAG pipelines and knowledge graphs (LangChain, LangGraph, LlamaIndex, OpenSearch, FAISS, Pinecone).
• Familiarity with agent operations logging and evaluation frameworks.
• Background in optimization: token cost reduction, chunking strategies, reranking, and retrieval latency tuning.
• Experience deploying models under quantized (int4/int8) and distributed multi-GPU inference.
• Exposure to open-vocabulary detection, zero/few-shot learning, multimodal RAG.
• Knowledge of temporal-spatial modeling (event/scene graphs).
• Experience deploying AI in edge or resource-constrained environments.
Company:
FieldAI is building general robot intelligence for the physical world. Founded in 2023, the company is headquartered in Mission Viejo, USA, with a team of 201-500 employees. The company is currently Growth Stage.

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