Software Engineer II

Crystal Equation Corporation; Menlo Park (CA); Hybrid; USD 90,000 - 96,000; Full time

Benefits offered by this job

Paid time off

Medical/dental/vision/Life insurance

401K

Parental leave

More

Job summary

Crystal Equation Corporation is seeking a Software Engineer II for a remote role in the United States. You will develop Python scripts to automate generation of videos across quality levels, interact with an internal lab API, and build scalable pipelines using FFmpeg to apply post-processing and evaluate quality metrics.

Requirements include a Bachelor's degree in computer science or related field, experience with REST/internal APIs, and familiarity with video quality metrics and testing

Qualifications

• Bachelor's degree in computer science, software engineering or relevant field.
• Experience writing Python scripts for automation.
• Experience interfacing with REST/internal APIs and media processing toolkits like FFmpeg.

Responsibilities

• Develop Python scripts and automation workflows to generate videos at different quality levels.
• Interact with internal lab API to build baseline video sets on real devices.
• Create pipelines to apply post-processing (FFmpeg) on videos and evaluate quality metrics.

Skills

Python

REST APIs

FFmpeg

Data pipelines

Education

Bachelor's degree in computer science or related field

Tools

FFmpeg

Job description

Software Engineer II Remote

Summary We will be conducting a comprehensive video rating study for our apps. The goal of this study is to establish robust technical metrics that will enable iterative system improvements - helping with distilling videos and images that are representative of what people would share on our apps.

Goal of the engagement

Goal of the engagement We want to create a corpus of app images and videos, and their encoded versions, that can be used to create technical metrics that we can use to:

Responsibilities

• Evaluate various video/image datasets and get a set representative
• Generate a corpus of encoded videos from the reference set
• Develop a scalable pipeline to apply various post-processing algorithms on top of lab generated videos (will require using FFMPEG, GPU shaders)
• Validate videos generated meet a certain bar
• Sanitize the ratings data coming from Appen raters (crowd sourcing) and do some sanity checks (we already have scripts to SUREAL normalization etc)
• A third responsibility is to compare the human ratings with current technical metrics and identify patterns where current technical metrics dont capture human ratings. Generate more videos following this pattern so that we can improve technical metrics
• E.g: When CW gets back ratings from Appen, they could look for cases (after validation) where UVQ differs from ratings. Use this to find/identify gaps in UVQ and propose data needed to fix the gaps (beyond the 3 identified known gaps today).
• Since the CW would be creating a lot of different videos, they could have a validation set of expected metric movements and find cases where UVQ does not match expected movements. Use this to find gaps in UVQ and propose data needed to fix the gaps
• Write Python scripts and implement large-scale automation workflows to generate videos across various quality levels.
• Interact with an internal "lab API" to build baseline video sets using real devices on the app stack.
• Build automated workflows to introduce specific video quality artifacts/differences (e.g., exposure adjustments) using tools like FFmpeg.
• Perform manual spot checks on generated video samples to maintain data integrity and test quality.
• Ingest, review, and analyze video ratings and raw media returned from external rating vendors to generate reporting on technical metrics.
• Develop Python scripts to transform and format rating data for downstream machine learning application ingestion.

Requirements

• Ability to write Python scripts and run large scale automation to generate videos at different qualities. This includes interacting with an internal "lab API" to create a baseline set of videos using real devices on WA stack as well, as automated workflows to add additional video quality differences to the generated videos. For instance, adjust the exposure. The latter might require familiarity with tools like FFmpeg.
• Experience interfacing with REST/internal APIs and working with media processing toolkits like FFmpeg to programmatically manipulate video properties (e.g., exposure, quality parameters).
• Analytical capability to interpret external vendor rating data, track quality metrics, and compile performance reports.
• Attention to detail for conducting manual quality assurance and spot-checking generated media.
• Bachelor's degree in computer science, software engineering or relevant field required.
• Nice to Have Background in video quality assessment metrics (VMAF, PSNR, SSIM) or audio/video streaming protocols.
• Experience working with real-device testing frameworks or mobile stack automation (app stack specifically).
• Knowledge of data pipeline integrations for Machine Learning feature store inputs.
• Familiarity with AI coding tools to accelerate their work

Pay range is $65 - $70 per hour with full benefits available.

• paid time off
• medical/dental/vision/life insurance
• 401K
• parental leave
• more

Our compensation reflects the cost of labor across several US geographic markets.

Pay is based on several factors including market location and may vary depending on job-related knowledge, skills, and experience.

THE PROMISES WE MAKE

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Your recruiter will prep you for the interview, obtain feedback, guide you through any necessary paperwork and provide everything you need for a successful start.

We will serve to empower you along the way and provide the path for your professional journey.

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