About the job
Location: San Francisco / Palo Alto, California
Job Type: Full-time
About Us:
Avala is a visionary company at the forefront of the labor economy and AI revolution, operating as a two-sided marketplace. We are dedicated to reshaping the future of work while ensuring our AI systems are closely aligned with human values.
Our innovative platform facilitates the integration of human feedback into AI processes, distinguishing itself from traditional crowdsourcing methods. By dynamically orchestrating digital assembly lines with the efficiency of a Gigafactory, Avala adeptly meets the needs of complex data labeling and high-volume content moderation on a global scale.
We recognize the transformative potential of AI in society and are committed to fostering positive and inclusive changes. Our goal is to empower individuals and communities worldwide, providing them with the opportunity to contribute to the curation and moderation of AI systems.
Meet Our Founder:
Founded in May 2020 by Emal Alwis, a seasoned expert from Tesla Autopilot, Avala is on a mission to tackle pressing global challenges by offering dignified digital work opportunities. We believe that every individual deserves access to equitable wages, regardless of their location, simply through the use of a smartphone.
Position Overview:
As a Lead Backend Software Engineer on the Avala Engineering team, you will play a crucial role in designing and implementing a variety of tools that power our Digital Assembly Line. This key function supports AI-assisted human labeling and diverse machine learning workflows.
Your contributions will significantly influence the lifecycle of Avala’s full-stack ML Ops platform, encompassing data curation, processing, discovery, annotation, and visualization. You will also develop tools that automate the complete workflow for training, validating, and deploying customer models into production.
We seek a backend engineer with outstanding software engineering capabilities who can make immediate contributions to our tooling systems. While familiarity with machine learning, computer vision, or neural networks is advantageous, it is not a prerequisite. A strong candidate will either be a skilled software generalist or a specialist eager to learn and grow within our dynamic environment.

