# Member of Technical Staff, Data & ML Infrastructure for Video Models

**Company:** [Cantina](https://hotfix.jobs/companies/cantina)
**Location:** Remote
**Role:** Data Engineering
**Salary:** $200k – $260k/yr
**Skills:** Python, Aws S3, DynamoDB, Kubernetes, PyTorch, Mturk, Prolific, Data Pipelines, Distributed Data Processing, Ml Preprocessing
**Posted:** 2026-04-02

> Builds and scales data pipelines for video generation models, including ingestion, annotation via MTurk/Prolific, preprocessing, and curation using Python, AWS, Kubernetes. Requires 3+ years in ML/data engineering, PyTorch experience, and cross-functional collaboration.

## Job Description

## Responsibilities
- Build and maintain data pipelines for large video generation models, including data ingestion, parsing, filtering, preprocessing, and dataset curation at scale, using tools such as **AWS S3** and **DynamoDB**.
- Design and run annotation workflows across platforms such as **MTurk**, **Prolific**, including task design, quality control, and label validation.
- Train, evaluate, and improve smaller supporting models used for data filtering, quality assessment, preprocessing, or other parts of the ML pipeline.
- Partner closely with research and engineering teams to turn experimental workflows into scalable, repeatable systems that support model training and evaluation.
- Own data quality across the pipeline by identifying bottlenecks, failure modes, and low-quality sources, and continuously improving tooling and processes.
- Build internal tools and automation that make it easier to prepare datasets, launch annotation jobs, monitor outputs, and support model development end to end.
- Drive larger pipeline projects from start to finish, such as new dataset creation efforts or upgrades to labeling and preprocessing infrastructure.
- Work within a **Kubernetes**-based training infrastructure, ensuring datasets are properly prepared, formatted, and delivered to training clusters.
- Profile and optimize research model inference scripts used in preprocessing steps, ensuring that model-driven filtering and transformation stages run within practical time and cost constraints when applied to large-scale raw data.

## Requirements
- **3+ years** of experience in machine learning, applied ML, data pipelines, or related engineering roles, ideally working on large-scale multimodal, video, or vision-based systems.
- Strong programming skills in **Python** and solid experience building reliable data processing and preprocessing pipelines for ML workflows.
- Hands-on experience preparing training data for ML models, including parsing, filtering, dataset curation, quality control, and large-scale data handling using tools such as **AWS S3** and **DynamoDB**.
- Familiarity with annotation and labeling workflows, including task design, vendor or crowd-platform orchestration such as **MTurk** or **Prolific**, and methods for ensuring label quality.
- Experience working with **Kubernetes** for orchestrating distributed workloads, including data preprocessing, pipeline execution, and dataset delivery to training clusters.
- Comfort working across cloud and on-demand compute environments such as **AWS** and **RunPod**, with the ability to port and optimize pipelines across infrastructure.
- Familiarity with distributed data processing frameworks and experience designing systems that operate reliably at scale across many nodes or workers.
- Working knowledge of **PyTorch** and the broader deep learning stack, with the ability to read, debug, and optimize research model inference code for use in production preprocessing pipelines.
- Ability to work cross-functionally with research and engineering teams and translate experimental ideas into robust, scalable systems.
- Bachelor's, Master's, or PhD in Computer Science, Machine Learning, Engineering, Mathematics, or a related technical field; experience in generative video, computer vision, or multimodal ML is strongly preferred.

## Nice-to-Haves
- Experience training, evaluating, or fine-tuning smaller ML models used for classification, filtering, ranking, quality assessment, or other supporting tasks in an ML pipeline.

## Compensation
- Anticipated annual base salary range: **$200,000-$260,000** (U.S.).

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