Data Engineer

"The AI company that's revolutionizing Hollywood

Flawless is changing the landscape of filmmaking by introducing innovative Gen AI film editing tools. Our goal is to equip filmmakers with advanced technology that fosters unrestricted creativity, broadens storytelling opportunities, and delivers unparalleled visual and emotional experiences. A focal point of our work is paving the way for ethical AI through the development of the Artistic Rights Treasury (A.R.T.), a rights management solution safeguarding artists and rights holders in the Entertainment realm.

Reports to: ML Engineering Manager

What we are looking for:

We are seeking a meticulous Data Engineer passionate about crafting platforms that significantly reduce the timeframe for transitioning Machine Learning research to production. The Data team's vision is to enable our cross-functional ML teams to dedicate most of their time to tackling complex ML problems, rather than dealing with data engineering, infrastructure, and operational challenges.

Responsibilities:

• Build and maintain data annotation pipelines, establish continuous data delivery and annotation workflows.
• Collaborate with our ML teams on constructing data transformation pipelines for expansive computer vision datasets.
• Create robust data quality metrics and enhance data quality and variety continually.
• Establish data management and exchange standardization.
• Support data sourcing endeavors, manage licenses, and contribute to data governance strategies.
• Work with the Platform team to ensure efficient data storage, transportation, and accessibility.

Qualifications:

• Solid analytical foundation with a BSc or MSc in data engineering/machine learning or related fields.
• Proficiency in Python programming.
• Experience developing and managing large-scale datasets for machine learning, including defining quality benchmarks.
• Proficiency in setting up infrastructure at scale for ML/Data Teams, including CI/CD & Data pipelines.
• Experience with cloud platforms (AWS, GCP, Azure) and familiarity with infrastructure as code.

Preferred Qualifications:

• Prior experience in an early-stage data function, pivotal in maturing the data team during tenure.
• Hands-on experience with the AWS Data Stack.
• Experience handling large-scale data, particularly in the Computer Vision domain.
• Familiarity with multi-stage data transform pipelines and large model training involving extensive content.

Salary: $160,000 - $185,000 per year

Interview Process:

At Flawless, we aim to provide a welcoming and insightful interview experience to help candidates showcase their best selves. Our interview process spans four rounds, including interactive sessions simulating real work scenarios.

• Recruiting Screen: Initial call with recruiters to discuss background, motivation, and fit for Flawless.
• Hiring Manager Screen: Zoom interview with Platform Engineering Leadership to evaluate technical expertise.
• Reverse System Design: Zoom meeting with Platform Engineering team members to delve into data transformation experience.
• Final Interview: Onsite session to engage with ML team members, focusing on data and ML operations.

Why work at Flawless?

Joining Flawless means contributing to a company culture built on trust, autonomy, and collaboration. This opportunity is ideal for individuals seeking to be part of a rapidly growing company in its most dynamic developmental phase. Embrace the chance to shape a caring, creative, and collaborative company culture.

Additional Benefits:

• Autonomy in work.
• Hybrid working arrangement.
• Competitive Salary.
• Stock Options.

Flawless values diversity and strives to offer an equal opportunity, secure environment where team members can excel. We are dedicated to providing fair employment opportunities, irrespective of race, color, ancestry, religion, sex, national origin, sexual orientation, age, citizenship, marital status, disability, gender identity, or Veteran status. Qualified applicants are welcome, regardless of criminal histories, in accordance with legal regulations."