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Our Purpose

At Fiddler, we understand the implications of AI and the impact that it has on human lives. Our company was born with the mission of building trust into AI. With the rise of the internet, trust in AI has been degraded by a plethora of issues like spam, fraudulent transactions, hate speech, and online abuse. Fiddler enables organizations to get ahead of these issues by building trustworthy, transparent, and explainable AI solutions.

Fiddler partners with AI-first organizations to help build a long-term framework for responsible AI practices, which, in turn, builds trust with their user base. Data Science, MLOps, and business teams use Fiddler AI to monitor, explain, analyze, and improve their AI solutions to identify performance gaps, mitigate bias, and drive better outcomes. Our platform enables engineering teams and business stakeholders alike to understand the “why” and how behind model outcomes.

Our Founders

Fiddler AI is founded by Krishna Gade (engineering leadership at Facebook, Pinterest, Twitter, and Microsoft) and Amit Paka (two-time founder with acquisitions by Samsung and PayPal and product roles at Expedia and Microsoft). We are backed by Insight Partners, Lightspeed Venture Partners, and Lux Capital.

Why Join Us

Our team is motivated to unlock the AI opaque box and help society harness the power of AI. Joining us means you get to make an impact by helping reduce algorithmic bias and ensure that models in production across many different industries are transparent and ethical. We are an early-stage startup and have a rapidly growing team of intelligent and empathetic doers, thinkers, creators, builders, and everyone in between. The AI and ML industry has a rapid pace of innovation and the learning opportunities here are monumental. This is your chance to be a trailblazer.

Fiddler is recognized as a pioneer in the field of AI Observability and has received 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 of 2020, and a 2019 Gartner Cool Vendor in Enterprise AI Governance and Ethical Response. By joining our brilliant (at least we think so) team, you will help pave the way in the AI Observability space.

We’re looking for a mission-driven and collaborative Automation Engineer to lead our quality-driven development efforts.

What We Are Looking For

Passion for Fiddler’s core mission of driving transparency, fairness and accountability in AI around the globe

Cross-functional team player, experience collaborating with Product and Engineering teams to solve complex customer problems

Proficiency in Python

A quality assurance engineer with an interest or background in Data Science or Machine Learning / AI

At least 4 years of experience in software quality assurance projects

Machine learning experience is a plus

Background in Computer Science, Data Science, Data Analysis or a related field

Startup experience is ideal

Natural curiosity and willingness to learn

Experience in functional, performance and systems testing methodologies

Experience in test automation, standard QA and tools; such as Jenkins, Selenium etc

Proven abilities in bug detection, regression and defect tracking

Must be able to write automation code

What You'll Do

Feature, patch and release testing of our application in our SaaS environment

Write and execute plans for defining and improving quality metrics

Build, maintain and execute automation scripts for functional, regression, loading and stress testing

Set up CI/CD tools as part of the engineering efforts

Build and run software testing operations including data scientists workflows, API and UI testing

Coordinate with the broader software development, machine learning, product management and technical support teams to make sure our quality standards are maintained in a timely and efficient manner

Work closely with backend, infrastructure, UI and data science to perform job duties

Drive software quality processes, improve, and report software quality metrics

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