Actively experiment with emerging techniques like PEFT (LoRA/QLoRA), Quantization, and Vector ... Hands-on experience with at least one major deep learning framework, such as PyTorch, and ...
Actively experiment with emerging techniques like PEFT (LoRA/QLoRA), Quantization, and Vector ... Hands-on experience with at least one major deep learning framework, such as PyTorch, and ...
... product quantization is a plus. * Experience with embeddings, ANN/KNN, vector stores, database ... Foundational understanding of Natural Language Processing and Deep Learning. * Excellent problem ...
... product quantization is a plus. * Experience with embeddings, ANN/KNN, vector stores, database ... Foundational understanding of Natural Language Processing and Deep Learning. * Excellent problem ...
... product quantization is a plus. * Experience with embeddings, ANN/KNN, vector stores, database ... Foundational understanding of Natural Language Processing and Deep Learning. * Excellent problem ...
... product quantization is a plus. * Experience with embeddings, ANN/KNN, vector stores, database ... Foundational understanding of Natural Language Processing and Deep Learning. * Excellent problem ...
Deep Learning Quantization information
What are the key skills and qualifications needed to thrive as a Deep Learning Quantization Engineer, and why are they important?
To excel as a Deep Learning Quantization Engineer, you need a strong background in machine learning, applied mathematics, and computer science, usually supported by an advanced degree in a related field. Familiarity with deep learning frameworks (such as TensorFlow or PyTorch), quantization toolkits, and hardware acceleration platforms is crucial. Analytical thinking, problem-solving, and clear technical communication are standout soft skills in this role. These abilities are essential for efficiently optimizing models for deployment on resource-constrained hardware while maintaining accuracy and performance.
What are some common challenges faced when implementing deep learning quantization in production environments?
One of the main challenges in implementing deep learning quantization is balancing model accuracy with computational efficiency, as quantization can sometimes lead to a drop in model performance. Additionally, ensuring hardware compatibility and optimizing for different devices (such as CPUs, GPUs, or edge devices) can require extensive testing and tuning. Collaboration with data scientists, software engineers, and hardware specialists is often essential to successfully deploy quantized models at scale. Staying updated with the latest quantization techniques and frameworks is also important for overcoming these challenges.
What is deep learning quantization?
Deep learning quantization is the process of reducing the precision of the numbers used to represent a neural network's parameters, activations, or both. By converting the typically used 32-bit floating-point values to lower bit-width formats such as 16-bit or 8-bit integers, quantization significantly reduces the memory footprint and computational requirements of deep learning models. This technique helps deploy models efficiently on edge devices and mobile hardware while maintaining acceptable accuracy levels. Quantization is widely used in model optimization for faster inference and lower power consumption.
What is the difference between Deep Learning Quantization vs Machine Learning Engineer?
| Aspect | Deep Learning Quantization | Machine Learning Engineer |
|---|---|---|
| Required Credentials | Advanced degrees in AI, Computer Science, or related fields; knowledge of neural networks | Bachelor's or Master's in CS, Data Science, or related fields; programming skills |
| Work Environment | Research labs, AI development teams, hardware optimization settings | Software development teams, data-driven projects, product-focused environments |
| Industry Usage | AI hardware optimization, model deployment, edge computing | Model development, data analysis, software solutions across industries |
Deep Learning Quantization focuses on reducing model size and improving inference speed through techniques like weight and activation quantization, often in hardware or embedded systems. Machine Learning Engineers develop, implement, and optimize machine learning models for various applications. While both roles require knowledge of AI and programming, Deep Learning Quantization is more specialized in model optimization techniques, whereas Machine Learning Engineers work broadly on model development and deployment.
What are popular job titles related to Deep Learning Quantization jobs in Connecticut? For Deep Learning Quantization jobs in Connecticut, the most frequently searched job titles are:
What job categories do people searching Deep Learning Quantization jobs in Connecticut look for? The top searched job categories for Deep Learning Quantization jobs in Connecticut are:
Full-time
Posted 26 days ago
Walt Disney Company rating
7.6
Based on 124 frontline employees who took The Breakroom Quiz
6th of 47 rated entertainment
Job description
Job Posting Title:
Lead Data Scientist, Content IntelligenceReq ID:
10145516Job Description:
Department/Group Overview:
Disney Entertainment and ESPN Product & Technology
Technology is at the heart of Disney's past, present, and future. Disney Entertainment and ESPN Product & Technology is a global organization of engineers, product developers, designers, technologists, data scientists, and more - all working to build and advance the technological backbone for Disney's media business globally.
The team marries technology with creativity to build world-class products, enhance storytelling, and drive velocity, innovation, and scalability for our businesses.We are Storytellers and Innovators. Creators and Builders. Entertainers and Engineers. We work with every part of The Walt Disney Company's media portfolio to advance the technological foundation and consumer media touch points serving millions of people around the world.
Here are a few reasons why we think you'd love working here:
1. Building the future of Disney's media: Our Technologists are designing and building the products and platforms that will power our media, advertising, and distribution businesses for years to come.
2. Reach, Scale & Impact: More than ever, Disney's technology and products serve as a signature doorway for fans' connections with the company's brands and stories. Disney+. Hulu. ESPN. ABC. ABC News...and many more. These products and brands - and the unmatched stories, storytellers, and events they carry - matter to millions of people globally.
3. Innovation: We develop and implement groundbreaking products and techniques that shape industry norms and solve complex and distinctive technical problems.
Content Platforms & Operations is responsible for the development and ongoing advancement of the technical and operational functions driving the worldwide distribution and monetization of Disney's linear networks and theatrical content. This includes Disney's media supply chain and storage, network and theatrical distribution operations, media localization, network transmission and origination, and more. The global team also plays a critical role as the primary collaboration point for Product & Technology with Disney's EMEA, APAC, and LATAM regional business teams.
Job Summary:
The Content Platforms team manages a diverse suite of products and services that power Disney's media supply chain, enabling content delivery for iconic brands like Disney+, ESPN+, Hulu, ABC, Marvel, and Star Wars.
We are seeking a highly skilled Lead Data Scientist to help us develop and deploy techniques that can understand our media. This is a hands-on role for a builder and systems thinker who can take ideas from a concept to a functional, high-performance prototype with speed and ingenuity.
In this role you will:
- Collaborate with product and technology teams across the globe.
- Conceptualize and co-develop innovative AI/ML solutions to address complex business challenges in the media space, contributing to a culture of rapid innovation.
- Identify systematic frameworks for prompt engineering and evaluation, ensuring VLM outputs meet strict ground truth requirements.
- Develop rigorous evaluation metrics (beyond standard accuracy) to validate model performance for temporal tasks
- Bridge experimentation and production through partnership with product and engineering teams, ensuring experimental models are aligned with business needs, optimized for inference and can be seamlessly integrated into our cloud platforms.
- Establish and refine best practices for the application of generative and non-generative AI/ML techniques, ensuring optimal performance for both experimental and production use cases.
- Develop statistical analyses to validate model performance and business impact.
- Actively experiment with emerging techniques like PEFT (LoRA/QLoRA), Quantization, and Vector embeddings-based media retrieval
Responsibilities and Duties of the Role:
- Create and Maintain Data Science Solutions: Design, create, test, and document prototypes aligned with business objectives and engineering standards, on a clear path to production.
- Collaborate with Cross-Functional Teams: Work closely with software engineers and product teams to ensure data science solutions effectively support business goals and content strategies.
Required Education, Experience/Skills/Training:
We're looking for a candidate that meets these basic requirements:
- Bachelor's in Computer Science, Data Science, or a related scientific/engineering field.
- At least 7 years of relevant industry experience in data science.
- Proficient in Python, and extensive experience with libraries such as Pandas, NumPy, and scikit-learn.
- Hands-on experience with at least one major deep learning framework, such as PyTorch, and experience with LLM prompting and fine-tuning.
- Solid understanding of the full lifecycle of a machine learning project, from data exploration and feature engineering to model deployment and monitoring.
- Strong communication and presentation skills, with the ability to convey complex analytical findings to both technical and non-technical audiences.
- Experience with cloud platforms, including storage, compute, and ML services such as AWS S3, Bedrock, EC2, and SageMaker or Databricks.
After the basic requirements, below are our preferred requirements.
You don't need to meet all of these, but it would be advantageous to meet one or more:
- MS or PhD in Computer Science, Data Science, or a related scientific/engineering field.
- Previous work experience in media or entertainment technology, particularly in content distribution and the media supply chain.
- VLM / LLM model distillation.
- Expertise in advanced statistical techniques and experimental design.
- Experience with generative AI for media understanding, including video, image, and text analysis.
- Experience working with development teams across multiple regions, time zones, and cultures.
- A strong curiosity about how Disney delivers the Magic and a desire to be a part of it.
#DISNEYTECH
The hiring range for this position is $155,700 - $208,700 per year. The base pay actually offered will take into account internal equity and also may vary depending on the candidate's geographic region, job-related knowledge, skills, and experience among other factors. A bonus and/or long-term incentive units may be provided as part of the compensation package, in addition to the full range of medical, financial, and/or other benefits, dependent on the level and position offered.Job Posting Segment:
Content Platforms & OperationsJob Posting Primary Business:
CPO - Content PlatformsPrimary Job Posting Category:
Data ScienceEmployment Type:
Full timePrimary City, State, Region, Postal Code:
Glendale, CA, USAAlternate City, State, Region, Postal Code:
Date Posted:
2026-04-02What Walt Disney Company employees say
Pay
Benefits
Hours and flexibility
Workplace
Get the full story on Breakroom
About Walt Disney
Sourced by ZipRecruiter
At Disney, we're storytellers. We make the impossible, possible. We do this through utilizing and developing cutting-edge technology and pushing the envelope to bring stories to life through our movies, products, interactive games, parks and resorts, and media networks. Now is your chance to join our talented team that delivers unparalleled creative content to audiences around the world. "We create happiness." That's our motto at Walt Disney Parks and Resorts. And it permeates everything we do. At Disney, you'll help inspire that magic by enabling our teams to push the limits of entertainment and create the never-before-seen!
Industry
Amusement, gambling, and recreation
Company size
10,000+ Employees
Headquarters location
Burbank, CA, US