Join Fiddler AI as a Staff AI Scientist

Our Purpose

At Fiddler AI, we recognize the transformative potential of Artificial Intelligence (AI) and its profound impact on society. Our mission is to embed trust into AI systems. As the internet has evolved, trust in AI has been compromised by issues such as spam, fraudulent activities, hate speech, and online abuse. Fiddler AI equips organizations with the tools to build trustworthy, transparent, and explainable AI frameworks.

We collaborate with AI-first organizations to establish long-term responsible AI practices, enhancing trust with their user base. Data Science, MLOps, and business teams leverage Fiddler AI to monitor, explain, analyze, and refine their AI systems, targeting performance gaps, mitigating bias, and driving superior outcomes. Our platform empowers both engineering teams and business stakeholders to comprehend the intricacies behind model outputs.

Our Founders

Fiddler AI was co-founded by Krishna Gade, with engineering leadership experience at Facebook, Pinterest, Twitter, and Microsoft, and Amit Paka, a serial entrepreneur with startups acquired by Samsung and PayPal and product roles at Expedia and Microsoft. We are supported by Insight Partners, Lightspeed Venture Partners, and Lux Capital.

Why Join Us

Our dedicated team aims to demystify AI and facilitate ethical AI utilization. By joining Fiddler AI, you will significantly impact reducing algorithmic bias and ensuring transparency and ethics in AI models used across various industries.

As an early-stage startup, we boast a rapidly expanding team of innovative and compassionate thinkers, builders, and creators. Within Fiddler, there are ample opportunities for growth. The AI and ML industry is fast-paced and teeming with learning possibilities. This is your chance to be a trailblazer.

Fiddler AI is celebrated as a pioneer in Model Performance Management (MPM) and has earned numerous accolades, including:

• 2022 a16z Data50 list
• 2021 CB Insights AI 100 most promising startups
• 2020 WEF Technology Pioneer
• 2020 Forbes AI 50 most promising startups
• 2019 Gartner Cool Vendor in Enterprise AI Governance and Ethical Response

What You'll Do

As a Staff AI Scientist at Fiddler AI, your responsibilities will include:

• Developing and fine-tuning novel ML models, metrics, and techniques to classify and explain ML models and LLM prompts/responses.
• Establishing processes around model building, deployment, and MLOps to ensure the health and performance of production artifacts.
• Driving thought-leadership by staying current on responsible AI and Gen AI research, experimenting with and benchmarking novel techniques related to model monitoring, explainability, fairness, and more.
• Identifying potential product offerings from research and guiding related efforts.
• Developing a thorough product understanding to recognize common customer challenges and devising powerful generalizable solutions.
• Supporting Sales and Customer Success teams in addressing advanced customer queries requiring deep ML expertise.
• Providing expert guidance to customers with challenging explainability use cases.
• Authoring white papers, blog posts, and developing technical content around ML and the Fiddler platform.

What We're Looking For

The ideal candidate will have:

• An MS/PhD in Computer Science, Data Science, or a related field.
• 7-8 years of industry experience.
• Proficiency in Python and experience writing production-grade code to support ML model deployments handling live workloads.
• Experience fine-tuning ML models in production, focusing on performance, latency, and model size/quantization.
• Familiarity with common LLM frameworks (e.g., Langchain, Llamaindex, RAG, HuggingFace) and evaluation frameworks (e.g., Ragas, Presidio).
• Ability to conduct deep analysis and benchmarking of models and methods.
• Experience debugging and troubleshooting production systems.
• Bonus points for experience speaking at industry events, working with computer vision models, data visualization, dimensionality reduction techniques, and infrastructure management (AWS/Google Cloud, Kubernetes, or equivalent on-premises technologies).