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Staff Backend Engineer

6 Months ago • 8-15 Years • Artificial Intelligence

Job Summary

Job Description

Fiddler seeks a Staff Backend Engineer with 8+ years of experience in designing and building cloud platforms for enterprise ML model monitoring and analysis. Strong proficiency in Python, infrastructure management (AWS/Google Cloud, Kubernetes), and mentoring junior engineers is required.
Must have:
  • AWS/Google Cloud
  • Kubernetes experience
  • Python proficiency
  • Backend Engineering
Good to have:
  • ML/MLops experience
  • API Design
  • Engineering Best Practices
  • Multinational Teams
Perks:
  • Growth Opportunities
  • Impactful Work

Job Details

About the job

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, 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. Within Fiddler, there is ample opportunity for growth. 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 Model Performance Management (MPM) 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 MPM space.

What We are Looking For:

  • Bachelors or above in Computer Engineering, Computer Science, Information Systems, or a related field
  • 8+yrs of experience
  • Deep Knowledge of infrastructure management (AWS / Google Cloud, Kubernetes, or equivalent on-premises technologies)
  • Proficiency with Python
  • Someone who values and enjoys mentoring and coaching junior team members
  • Deep knowledge of engineering best practices and experience scaling an enterprise engineering organization

Nice To Have

  • Experience working in ML/MLops

What You’ll Do

  • Design and build a world-class cloud platform to help enterprises monitor and analyze their ML models.
  • Design enterprise-grade, scalable APIs to plug into customers’ ML workflows.
  • Work with product and customer facing teams to enhance and expand our product.
  • Recommend and implement engineering best practices, improve the functionality of existing software, and ensure that the design, application, and maintenance of software meets quality standards
  • Help construct and implement best practices across multinational teams
  • Architecting complex, multitenant systems
  • Uplevel our engineering team and help to improve our core quality

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