1

Interpretability Ai Jobs in Riverside, CA (NOW HIRING)

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

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 ...

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 ...

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 ...

Interpretability Ai information

See Riverside, CA salary details

$46.4K

$135.3K

$185.2K

How much do interpretability ai jobs pay per year?

As of Jun 10, 2026, the average yearly pay for interpretability ai in Riverside, CA is $135,329.00, according to ZipRecruiter salary data. Most workers in this role earn between $119,500.00 and $143,400.00 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.
What job categories do people searching Interpretability Ai jobs in Riverside, CA look for? The top searched job categories for Interpretability Ai jobs in Riverside, CA are:
What cities near Riverside, CA are hiring for Interpretability Ai jobs? Cities near Riverside, CA with the most Interpretability Ai job openings:

Agentic AI/ML Engineer, Multimodal

Field AI

Irvine, CA

Other

Posted 20 days ago


Job description

Field Foundation Model AI/ML Engineer

FieldAI's Irvine team is where embodied AI meets real robots, real sensors, and real field deployments. Based in the heart of Southern California's robotics ecosystem, we build risk-aware, reliable, field-ready AI systems that solve the hardest problems in robotics and unlock the full potential of embodied intelligence. If you want your work to ship, get tested on hardware, and improve through real deployments, Irvine is the place. We go beyond typical data-driven approaches or pure transformer-only architectures, combining rigorous engineering with learning systems proven in globally deployed solutions that deliver results today and get better every time our robots run in the field.

Field AI is transforming how robots interact with the real world. We are building risk-aware, reliable, and field-ready AI systems that address the most complex challenges in robotics, unlocking the full potential of embodied intelligence. We go beyond typical data-driven approaches or pure transformer-based architectures, and are charting a new course, with already-globally-deployed solutions delivering real-world results and rapidly improving models through real-field applications.

About the Job

Our Field Foundation Model (FFM) powers a global fleet of autonomous robots that capture massive streams of multimodal data across diverse, dynamic environments every day. As part of the Insight Team our mission is to transform this raw, multimodal data into actionable insights that empower our customers and engineers to deliver value. Field-insight Foundation Model (FiFM) is at the core of how we transform multimodal data from autonomous robots into actionable insights. As an AI/ML Engineer on the FiFM team, you will drive research and model development for one of Field AI's most ambitious initiatives. Your work will span computer vision, vision-language models (VLMs), multimodal scene understanding, and long-memory video analysis and search, with a strong emphasis on agentic AI (tool use, memory, multimodal retrieval-augmented generation). This is a full-cycle ML role: you'll curate datasets, fine-tune and evaluate models, optimize inference, and deploy them into production. It's a blend of applied research and engineering, requiring creativity, rapid experimentation, and rigorous problem-solving. While FiFM is your primary focus, you'll also contribute to broader perception and insight-generation initiatives across Field AI.

What You'll Get To Do:
  • 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.
What You Have:
  • 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.
The Extras That Set You Apart:
  • 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.

Our salary range is generous and we consider each individual's background and experience when determining final compensation. Base pay may vary based on role scope, job-related knowledge, skills, experience, and the Irvine, California market.

Why Join FieldAI in Irvine?

In Irvine, you will work where the robots are. Our local team builds and tests systems on real hardware with real sensors, then ships them to operate in unstructured, previously unknown environments around the world. We are solving one of robotics' hardest challenges: reliable deployment outside the lab. Our Field Foundational Models™ raise the bar for perception, planning, localization, and manipulation, with an emphasis on explainability and safety for real-world use.

You will collaborate with a world-class team that thrives on creativity, resilience, and bold thinking. We bring deep experience from organizations such as DeepMind, NASA JPL, Boston Dynamics, NVIDIA, Amazon, Tesla Autopilot, Cruise, Zoox, Toyota Research Institute, and SpaceX, along with a track record of field deployments and strong performance in DARPA challenge segments.

Be Part of the Next Robotics Revolution

We are looking for builders who want their work to leave the whiteboard and show up on robots. If you enjoy tackling tough, uncharted questions and working across disciplines, you will find your people here. Our teams span AI, software, robotics engineering, product, field deployment, and technical communication, all focused on shipping systems that perform in the real world.

Our headquarters is in Irvine, and we partner closely with teams there as well as colleagues across the US and around the world. Join us in Southern California and help define what dependable, field-ready autonomy looks like.

We value diverse perspectives and are committed to fostering an inclusive workplace. We evaluate candidates and employees based on merit, qualifications, and performance, and we do not discriminate on the basis of race, color, gender, national origin, ethnicity, veteran status, disability status, age, sexual orientation, gender identity, marital status, or any other legally protected status.