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Deep Learning Quantization Jobs in New York (NOW HIRING)

Associated Press

Machine Learning Engineer

Manhattan, NY · On-site

$145K - $180K/yr

Demonstrated ownership of deep-learning inference optimization in production (quantization, distillation, compilation, kernel/profile-level performance work) for transformer NLP and/or CV models.

Associated Press

Machine Learning Engineer

New York, NY · Hybrid

$145K - $180K/yr

Demonstrated ownership of deep-learning inference optimization in production (quantization, distillation, compilation, kernel/profile-level performance work) for transformer NLP and/or CV models.

Associated Press

Machine Learning Engineer

Manhattan, NY · Hybrid

$145K - $180K/yr

Demonstrated ownership of deep-learning inference optimization in production (quantization, distillation, compilation, kernel/profile-level performance work) for transformer NLP and/or CV models.

Clearview AI

Senior Machine Learning Engineer

Manhattan, NY · On-site

$115K - $158K/yr

... quantization, and GPU acceleration * Read current research, prototype novel algorithms from ... Strong experience using PyTorch, JAX, or other deep learning frameworks to develop and optimize ...

1Kosmos

AI / Machine Learning Engineer

Iselin, NJ · Hybrid

Strong understanding of deep learning architectures for image and text recognition. * Familiarity ... Preferred Qualifications * Experience with model quantization and optimization for mobile ...

1Kosmos

AI / Machine Learning Engineer

Iselin, NJ · On-site

Strong understanding of deep learning architectures for image and text recognition. * Familiarity ... Preferred Qualifications * Experience with model quantization and optimization for mobile ...

1Kosmos

AI / Machine Learning Engineer

Iselin, NJ · On-site

Strong understanding of deep learning architectures for image and text recognition. * Familiarity ... Preferred Qualifications * Experience with model quantization and optimization for mobile ...

Meta

Research Scientist - Reality Labs

New York, NY · On-site

$184K - $257K/yr

... on experience with deep learning frameworks such as PyTorch • Experience developing machine ... model optimization (quantization, sparsity, distillation, knowledge distillation) • Deep ...

Meta

Research Scientist - Reality Labs

New York, NY

$184K/yr

Explore and adopt novel model optimization, quantization, and efficiency techniques for resource ... Programming experience in Python and hands-on experience with deep learning frameworks such as ...

Norbert Health

Computer Vision/ML Engineer

Brooklyn, NY · On-site

$117K - $138K/yr

The position We are looking for our lead deep learning engineer to spearhead the development of our ... Optimize models for embedded deployment using quantization, pruning, TensorRT, and NVIDIA Triton

Norbert Health

Computer Vision/ML Engineer

New York, NY · On-site

$122K - $143K/yr

The position We are looking for our lead deep learning engineer to spearhead the development of our ... Optimize models for embedded deployment using quantization, pruning, TensorRT, and NVIDIA Triton

Norbert Health

Computer Vision/ML Engineer

New York, NY · On-site

$122K - $143K/yr

The position We are looking for our lead deep learning engineer to spearhead the development of our ... Optimize models for embedded deployment using quantization, pruning, TensorRT, and NVIDIA Triton

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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.
ML Research Scientist -Deep Learning & Transformer Architectures

ML Research Scientist -Deep Learning & Transformer Architectures

Millennium Management LLC

New York, NY • On-site

$150K - $200K/yr

Full-time

Re-posted 6 days ago

Millennium Management rating

7.7

Company rating: 7.7 out of 10

Based on 11 frontline employees who took The Breakroom Quiz

Job description

ML Research Scientist -Deep Learning & Transformer Architectures
Please direct all resume submissions to QuantTalentUS@mlp.com and reference REQ-29605 in the subject.
Overview
As part of a long-term research agenda within a newly formed systematic equities pod, we are building a proprietary Transformer-based model trained on tokenized intraday market data for next-token prediction of price movements.
We are seeking an exceptional ML research scientist with deep expertise in Transformer architectures and large-scale model training. You will design, implement, and train a custom decoder-only Transformer from scratch -not fine-tune an existing LLM, but build a purpose• built architecture for financial time-series.
This is a long-term research project with significant computational resources. The successful candidate will have a PhD in machine learning or a related field and demonstrated ability to implement Transformer architectures from first principles.
Principal Responsibilities
• Design and implement a custom decoder-only Transformer architecture optimized for tokenized financial time-series data
• Develop a novel tokenization scheme for intraday market data: price movements, volume, order flow, and cross-sectional features
• Implement efficient training pipelines using PyTorch with mixed-precision training, gradient checkpointing, and multi-GPU parallelism
• Design attention mechanisms adapted to financial data: temporal attention patterns, cross-asset attention, and multi-scale representations
• Build evaluation frameworks for next-token prediction accuracy, signal quality, and trading performance
• Implement inference optimization for low-latency production deployment: model quantization, KV-cache, speculative decoding
• Conduct rigorous ablation studies to validate architecture choices and training methodology
• Collaborate with the team to integrate model predictions into the live trading pipeline
• Document research methodology, experimental results, and architectural decisions
Required Skills / Qualifications
• PhD in Machine Learning, Computer Science, Statistics, Applied Mathematics, or a related field with a focus on deep learning
• Demonstrated ability to implement Transformer architectures from scratch (not just finetuning pre-trained models)
• Deep understanding of attention mechanisms, positional encodings, tokenization strategies, and training dynamics
• Expert-level PyTorch skills including custom modules, training loops, mixed-precision, and multi-GPU training
• Strong mathematical foundations: linear algebra, probability theory, optimization, information theory
• Experience training models at scale (100M+ parameters)
• Strong programming skills in Python and C++ for performance-critical components
• Self-directed researcher capable of defining and executing a multi-month research agenda
• Familiarity with Al-assisted development tools (Cursor, Claude Code)
Preferred Skills / Experience
• Experience applying deep learning to financial data or time-series forecasting
• Familiarity with tokenizatlon approaches for continuous or non-text data
• Published research in top ML venues (NeurlPS, ICML, ICLR) or equivalent industry experience
• Knowledge of market microstructure and intraday trading dynamics
• Experience with model compression, quantization, and inference optimization
Millennium offers a total compensation package which includes a base salary, discretionary performance bonus, and comprehensive benefits. The estimated base salary range for this position is $150,000 to $200,000, which is specific to New York and may change in the future. When finalizing an offer, we take into consideration an individual's experience level and the qualifications they bring to the role to formulate a competitive total compensation package.

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