ZipRecruiter

Log In

1

Deep Learning Quantization Jobs in New York (NOW HIRING)

Invoca

Senior ML Engineer

New York, NY · On-site +1

$114K - $157K/yr

Advanced Python and deep learning proficiency (PyTorch, HuggingFace Transformers, spaCy ... models via quantization, batching, and throughput tuning * Proficiency with inference ...

Photon

Sr Gen AI Engineer - NY/NJ

Manhattan, NY · On-site

The ideal candidate blends deep machine learning expertise with modern software engineering ... Knowledge of model fine-tuning techniques and local LLM quantization/hosting. Familiarity with ...

Photon

Sr Gen AI Engineer - NY/NJ

Manhattan, NY · On-site

The ideal candidate blends deep machine learning expertise with modern software engineering ... Knowledge of model fine-tuning techniques and local LLM quantization/hosting. Familiarity with ...

Vatic Labs

AI Researcher

New York, NY · On-site

$175K - $250K/yr

... data analysis, vector quantization, decision tree methods, EM methods, Bayesian methods ... Demonstration of deep knowledge of large language models and deep neural networks for practical ...

Vatic Labs

AI Researcher

New York, NY · On-site

$175K - $250K/yr

... data analysis, vector quantization, decision tree methods, EM methods, Bayesian methods ... Demonstration of deep knowledge of large language models and deep neural networks for practical ...

Vatic Labs

AI Researcher - Vatic Labs

Manhattan, NY · On-site

$175K - $250K/yr

... data analysis, vector quantization, decision tree methods, EM methods, Bayesian methods ... Demonstration of deep knowledge of large language models and deep neural networks for practical ...

Baseten

Software Engineer - Model Products

New York, NY · On-site

$180K - $360K/yr

... quantization, batching, and KV‑cache reuse. * Instrument deep observability (metrics, traces ... Exposure to a variety of ML startups, offering unparalleled learning and networking opportunities.

Normal Computing

Research Engineer, Inference

New York, NY · On-site

$250K - $325K/yr

Backed by $85M+ from the world's leading deep-tech investors and built by scientists, engineers ... Experience with inference optimization: quantization, sparsity, kernel fusion, or memory-efficient ...

Baseten

Engineering

New York, NY · On-site

$260K - $380K/yr

Deep personal background in GPU kernel engineering. You have written and shipped production CUDA ... Background in LLM inference kernels: attention variants, GEMMs, quantization (FP8/FP4), MoE routing

New

next page

Showing results 1-20

All JobsDeep Learning Quantization JobsDeep Learning Quantization Jobs in New York

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 is the difference between Deep Learning Quantization vs Machine Learning Engineer?

AspectDeep Learning QuantizationMachine Learning Engineer
Required CredentialsAdvanced degrees in AI, Computer Science, or related fields; knowledge of neural networksBachelor's or Master's in CS, Data Science, or related fields; programming skills
Work EnvironmentResearch labs, AI development teams, hardware optimization settingsSoftware development teams, data-driven projects, product-focused environments
Industry UsageAI hardware optimization, model deployment, edge computingModel 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?

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 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 are popular job titles related to Deep Learning Quantization jobs in New York? For Deep Learning Quantization jobs in New York, the most frequently searched job titles are:
What job categories do people searching Deep Learning Quantization jobs in New York look for? The top searched job categories for Deep Learning Quantization jobs in New York are:
What cities in New York are hiring for Deep Learning Quantization jobs? Cities in New York with the most Deep Learning Quantization job openings:
Deep Learning Quantization jobs near you
Infographic showing various Deep Learning Quantization job openings in New York as of July 2026, with employment types broken down into 74% Full Time, 24% Part Time, and 2% Contract. Highlights an 76% Physical, 2% Hybrid, and 22% Remote job distribution.
Compliance, New York, Vice President, AI/ML Engineer

Compliance, New York, Vice President, AI/ML Engineer

Goldman Sachs, Inc.

New York, NY • On-site

$196K - $253K/yr

Full-time

Re-posted 8 days ago

Goldman Sachs rating

8.2

Company rating: 8.2 out of 10

Based on 26 frontline employees who took The Breakroom Quiz

44th of 149 rated banks

Job description


VP - AI/ML Engineer - Compliance Engineering
YOUR IMPACT
Are you passionate about leveraging cutting-edge AI/ML techniques, including Large Language Models, to solve complex, mission-critical problems in a dynamic environment? Do you want to contribute to safeguarding a leading global financial institution?
OUR IMPACT
We are Compliance Engineering, a global team of engineers and scientists dedicated to preventing, detecting, and mitigating regulatory and reputational risks across Goldman Sachs. We build and operate a suite of platforms and applications that protect the firm and its clients.
We offer:
  • Access to petabyte scale of structured and unstructured data to fuel your AI/ML models, including textual data suitable for LLM applications.
  • The opportunity to work with state-of-the-art LLM models and agentic framework.
  • A collaborative environment where you can learn from and contribute to a team of experienced engineers and scientists.
  • The chance to make a tangible impact on the firm's ability to manage risk and maintain its reputation.

Within Compliance Engineering, we are seeking an experienced AI/ML Engineer to join our Engineering team. This role will focus on solving highly complex business problems using AI/ML techniques, incorporating latest emerging trends om building out vertical AI agents to run on data at massive scale.
HOW YOU WILL FULFILL YOUR POTENTIAL
As a member of our team, you will:
  • Design and architect scalable and reliable end-to-end AI/ML solutions specifically tailored for compliance applications, ensuring adherence to relevant regulatory requirements. This encompasses the development and implementation of GenAI-driven solutions, including agentic frameworks for automating compliance processes, RAG pipelines, and the creation and utilization of embeddings for compliance knowledge bases.
  • Explore diverse AI/ML problems, such as model fine-tuning, prompt engineering, and experimentation with different algorithmic approaches to address novel business challenges.
  • Develop, test, and maintain high-quality, production-ready code.
  • Lead technical projects from inception to completion, providing guidance and mentorship to junior engineers.
  • Collaborate effectively with compliance officers, legal counsel, and other stakeholders to understand business requirements and translate them into technical solutions.
  • Participate in code reviews to ensure code quality, maintainability, and adherence to coding standards. Promote best practices for AI/ML development, including version control, testing, and documentation.
  • Stay current with the latest advancements in AI/ML platforms, tools, and techniques to solve business problems.

QUALIFICATIONS
A successful candidate will possess the following attributes:[MR4]
  • A Bachelor's, Master's or PhD degree in Computer Science, Machine Learning, Mathematics, or a similar field of study.
  • Preferably 7+ years AI/ML industry experience for Bachelor's/Masters, 4+ years for PhD with a focus on Language Models.
  • Strong foundation in machine learning algorithms, including deep learning architectures (e.g., transformers, RNNs, CNNs)
  • Proficiency in Python and relevant libraries/frameworks such as TensorFlow, PyTorch, Hugging Face Transformers, scikit-learn.
  • Demonstrated expertise in GenAI techniques, including but not limited to Retrieval-Augmented Generation (RAG), model fine-tuning, prompt engineering, AI agents, and evaluation techniques.
  • Experience working with embedding models and vector databases.
  • Experience with MLOps practices, including model deployment, containerization (Docker, kubernetes), CI/CD, and model monitoring.
  • Strong verbal and written communication skills.
  • Curiosity, ownership and willingness to work in a collaborative environment.
  • Proven ability to mentor and guide junior engineers.

Experience in some of the following is desired and can set you apart from other candidates:
  • Experience with Agentic Frameworks (e.g., Langchain, AutoGen) and their application to real-world problems.
  • Understanding of scalability and performance optimization techniques for real-time inference such as quantization, pruning, and knowledge distillation.
  • Experience with model interpretability techniques.
  • Prior experience in code reviews/ architecture design for distributed systems.
  • Experience with data governance and data quality principles.
  • Familiarity with financial regulations and compliance requirements.

ABOUT GOLDMAN SACHS
At Goldman Sachs, we commit our people, capital and ideas to help our clients, shareholders and the communities we serve to grow. Founded in 1869, we are a leading global investment banking, securities and investment management firm. Headquartered in New York, we maintain offices around the world.
We believe who you are makes you better at what you do. We're committed to fostering and advancing diversity and inclusion in our own workplace and beyond by ensuring every individual within our firm has a number of opportunities to grow professionally and personally, from our training and development opportunities and firmwide networks to benefits, wellness and personal finance offerings and mindfulness programs. Learn more about our culture, benefits, and people at GS.com/careers.
We're committed to finding reasonable accommodations for candidates with special needs or disabilities during our recruiting process. Learn more: https://www.goldmansachs.com/careers/footer/disability-statement.html
© The Goldman Sachs Group, Inc., 2021. All rights reserved.
Goldman Sachs is an equal employment/affirmative action employer Female/Minority/Disability/Veteran/Sexual Orientation/Gender Identity

What Goldman Sachs employees say

Pay

Benefits

Hours and flexibility

Workplace

Get the full story on Breakroom

Goldman Sachs logo

About Goldman Sachs

Sourced by ZipRecruiter

At Goldman Sachs, we commit our people, capital and ideas to help our clients, shareholders and the communities we serve to grow. Founded in 1869, we are a leading global investment banking, securities and investment management firm. Headquartered in New York, we maintain offices around the world. We believe who you are makes you better at what you do. We're committed to fostering and advancing diversity and inclusion in our own workplace and beyond by ensuring every individual within our firm has a number of opportunities to grow professionally and personally, from our training and development opportunities and firmwide networks to benefits, wellness and personal finance offerings and mindfulness programs.

Industry

Finance and insurance

Company size

10,000+ Employees

Headquarters location

New York, NY, US

Year founded

1869

View All Goldman Sachs Jobs

Apply