Leverage state-of-the-art Natural Language Processing (NLP), deep learning, and generative AI ... Using KV Caching, Semantic Caching, Distributed Inference, Quantization, Low-latency API Design ...
Leverage state-of-the-art Natural Language Processing (NLP), deep learning, and generative AI ... Using KV Caching, Semantic Caching, Distributed Inference, Quantization, Low-latency API Design ...
Leverage state-of-the-art Natural Language Processing (NLP), deep learning, and generative AI ... Using KV Caching, Semantic Caching, Distributed Inference, Quantization, Low-latency API Design ...
Leverage state-of-the-art Natural Language Processing (NLP), deep learning, and generative AI ... Using KV Caching, Semantic Caching, Distributed Inference, Quantization, Low-latency API Design ...
Leverage state-of-the-art Natural Language Processing (NLP), deep learning, and generative AI ... Using KV Caching, Semantic Caching, Distributed Inference, Quantization, Low-latency API Design ...
Leverage state-of-the-art Natural Language Processing (NLP), deep learning, and generative AI ... Using KV Caching, Semantic Caching, Distributed Inference, Quantization, Low-latency API Design ...
... 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
See Columbus, OH salary details
$21.1K is the 25th percentile. Wages below this are outliers.
$10.6K - $22K
27% of jobs
$22K - $33.3K
0% of jobs
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$55.9K - $67.3K
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The median wage is $77.6K / yr.
$67.3K - $78.6K
25% of jobs
$78.6K - $89.9K
18% of jobs
$98K is the 75th percentile. Wages above this are outliers.
$89.9K - $101.2K
7% of jobs
$101.2K - $112.6K
2% of jobs
$112.6K - $123.9K
0% of jobs
$123.9K - $135.2K
21% of jobs
$10.6K
$81K
$135.2K
How much do deep learning quantization jobs pay per year?
What are the key skills and qualifications needed to thrive as a Deep Learning Quantization Engineer, and why are they important?
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 is deep learning quantization?
What are some common challenges faced when implementing deep learning quantization in production environments?

Full-time
Medical, Retirement
Posted 7 days ago
JPMorgan Chase & Co. rating
8.1
Based on 470 frontline employees who took The Breakroom Quiz
46th of 141 rated banks
Job description
DESCRIPTION:
Duties: Engage in cutting-edge initiatives to enhance virtual assistant capabilities. Leverage state-of-the-art Natural Language Processing (NLP), deep learning, and generative AI techniques to drive innovation and improve user interaction. Drive the development of scalable, production-grade AI solutions through experimentation with large language models (LLMs), small language models (SLMs) and domain-specific fine-tuning. Optimize small language models (SLMs) using advanced model fine-tuning techniques. Lead brainstorming sessions focused on NLP advancements, LLM fine-tuning strategies, and production deployment. Be involved in all aspects of machine learning, supporting tech, product teams, and providing expertise and guidance in machine learning applications. Perform monthly releases and model optimization contributing to the release cycle from an ML perspective to ensure continuous improvement of the assistant's capabilities. Own the machine learning solution for the transaction search application, which includes stakeholder management, continuous optimization, and research and innovation. Lead the build of a question-and-answer solution using advanced NLP techniques, allowing Chase customers to ask questions on the chase.com website. Conduct thorough analysis of business needs, exploring state-of-the-art research papers, deep learning models, and generative Al methods to inform solution design. Design and execute experiments using both large (LLM) and small language models (SLM) to enhance performance on targeted tasks. Utilize adapted model for the finance domain and optimize training efficiency. Lead Al Solution Development, stakeholder management, model release management, research on NLP Solutions to drive project success and deliver production-ready solutions.
QUALIFICATIONS:
Minimum education and experience required: Bachelor's degree in Computer Engineering, Computer Science, Information Technology, or a related field of study plus 7 years of experience in the job offered or as Applied AI ML Lead, Sr. Specialist - Data Sciences, Tech Lead III, Sr. Tech Lead - Data Sciences, Sr. Consultant, or related occupation. The employer will alternatively accept a Master's degree in Computer Engineering, Computer Science, Information Technology, or a related field of study plus 5 years of experience in the job offered or as Applied AI ML Lead, Sr. Specialist - Data Sciences, Tech Lead III, Sr. Tech Lead - Data Sciences, Sr. Consultant, or related occupation.
Skills Required: This position requires experience with the following: Utilizing Python to implement data science solutions, build scalable machine learning (ML) pipelines, and automate workflows; Applying Supervised and Unsupervised Learning to build predictive ML models, improve decision-making, and automate labeling; Using feature engineering to identify and select relevant features to improve ML model performance; Leveraging Hyperparameter Optimization to enhance ML model accuracy and generalization; Utilizing Neural Networks including Convolution Neural Networks (CNN), Recurrent Neural Networks (RNN), Long Short-Term Memory's (LSTM), and Transformers to build ML solutions, automate tasks, and fine-tune domain-specific language models; Using Open Source Embedding Models including transformers (all-mpnet-base-v2) and sentence transformers (msmarco-distilbert-base-tas-b) to capture the underlying semantic and contextual relationships in text data; Applying Tokenization, Named Entity Recognition, Semantic Search, and Topic Modeling to structure and analyze text data, improve user experience, and automate information retrieval; Using Prompt Engineering, System Prompt Design, Retrieval-Augmented Generation (RAG), Instruction Fine-Tuning, Parameter-Efficient Fine-Tuning, Multi-adapter Architectures, Domain Adaptation model training for Banking and Financial NLP, Synthetic Data Generation for Fine-Tuning and to Enhance LLM performance; Utilizing Dense Retrieval, Sparse Retrieval, Hybrid Search, Embedding-based Semantic Search to improve information retrieval accuracy and efficiency; Using Precision, Recall, F1, Mean Reciprocal Rank (MRR), Normalized Discounted Cumulative Gain (NDCG), SQUAD Metrics, Exact Match, Perplexity, Multi-class and Multi-label Evaluation, Latency Profiling, Human-in-the-loop Evaluation to assess and validate ML model effectiveness and performance; Utilizing Distributed Training using Data Parallel, Fully Sharded Data Parallel, Mixed Precision Training, Multi-GPU Scaling for LoRA (Low Rank Adapters), Fine-Tuning of SLMs (Small Language Models) to scale SLMs model training across hardware resources; Using KV Caching, Semantic Caching, Distributed Inference, Quantization, Low-latency API Design, Scaling LLM Serving on GPU and CPU to optimize SLMs (Small Language Models) inference speed, scalability, and resource utilization; Employing Snowflake, Databricks, and Sagemaker to manage data and ML model training.
Job Location: 1111 Polaris Pkwy, Columbus, OH 43240.
Full-Time.
Chase is a leading financial services firm, helping nearly half of America's households and small businesses achieve their financial goals through a broad range of financial products. Our mission is to create engaged, lifelong relationships and put our customers at the heart of everything we do. We also help small businesses, nonprofits and cities grow, delivering solutions to solve all their financial needs.
We offer a competitive total rewards package including base salary determined based on the role, experience, skill set and location. Those in eligible roles may receive commission-based pay and/or discretionary incentive compensation, paid in the form of cash and/or forfeitable equity, awarded in recognition of individual achievements and contributions. We also offer a range of benefits and programs to meet employee needs, based on eligibility. These benefits include comprehensive health care coverage, on-site health and wellness centers, a retirement savings plan, backup childcare, tuition reimbursement, mental health support, financial coaching and more. Additional details about total compensation and benefits will be provided during the hiring process.
We recognize that our people are our strength and the diverse talents they bring to our global workforce are directly linked to our success. We are an equal opportunity employer and place a high value on diversity and inclusion at our company. We do not discriminate on the basis of any protected attribute, including race, religion, color, national origin, gender, sexual orientation, gender identity, gender expression, age, marital or veteran status, pregnancy or disability, or any other basis protected under applicable law. We also make reasonable accommodations for applicants' and employees' religious practices and beliefs, as well as mental health or physical disability needs. Visit our FAQs for more information about requesting an accommodation.
Equal Opportunity Employer/Disability/Veterans
Our Consumer & Community Banking division serves our Chase customers through a range of financial services, including personal banking, credit cards, mortgages, auto financing, investment advice, small business loans and payment processing. We're proud to lead the U.S. in credit card sales and deposit growth and have the most-used digital solutions - all while ranking first in customer satisfaction.What JPMorgan Chase & Co. employees say
Pay
Benefits
Hours and flexibility
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About JPMorgan Chase & Co
Sourced by ZipRecruiter
Industry
Finance and insurance and banking and credit intermediation
Company size
10,000+ Employees
Headquarters location
New York, NY, US