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Deep Learning Quantization Jobs in Secaucus, NJ (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

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

$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.

Capgemini

Senior Software Engineer

New York, NY · On-site

$73K - $174K/yr

Apply reinforcement learning (RL) techniques to improve model performance and optimize outcomes ... Quantization * Pruning AI Operations & Production Systems * Deploy, troubleshoot, and maintain ...

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

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All JobsDeep Learning Quantization JobsDeep Learning Quantization Jobs in Secaucus, NJ

Deep Learning Quantization information

See Secaucus, NJ salary details

$11.2K

$85.3K

$142.3K

How much do deep learning quantization jobs pay per year?

As of Jul 17, 2026, the average yearly pay for deep learning quantization in Secaucus, NJ is $85,285.00, according to ZipRecruiter salary data. Most workers in this role earn between $73,200.00 and $141,300.00 per year, depending on experience, location, and employer.

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.
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Deep Learning Quantization jobs near you
AI Agents Applied Research/Engineering Lead - Vice President

AI Agents Applied Research/Engineering Lead - Vice President

JP Morgan Chase

Manhattan, NY • On-site

Full-time

Medical, Retirement

Re-posted 11 days ago

JPMorgan Chase & Co. rating

8.0

Company rating: 8.0 out of 10

Based on 491 frontline employees who took The Breakroom Quiz

58th of 149 rated banks

Job description

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. You'll have the opportunity to publish at top-tier venues like NeurIPS, ICML, and ACL-and see that research deployed to a user base of over 80 million customers.

As an AI Agents Applied Research/Engineering Lead in our  The Digital Team, you will work with the team  to shape how millions of customers discover, decide, and act-turning multi-step financial tasks into simple conversations. You'll lead the end-to-end lifecycle of LLM-based agents: defining research directions in areas like multi-step planning, tool use, and safety; building production systems that perform under real-world latency, accuracy, and compliance constraints; and partnering with Product, Engineering, Design, and Risk teams to bring those systems to market. The problems here are genuinely unusual-building AI that must be not just accurate but auditable, explainable, and safe in a highly regulated, high-stakes domain. Transform how millions of customers manage their money, make decisions, and get more from their financial relationships through a human-centered approach that blends cutting-edge AI with clear, trustworthy experiences. 

Job Responsibilities

  • Lead research and deployment of agentic AI systems with multi-step workflows, tool calling, and multi-agent orchestration.

  • Fine-tune and optimize LLMs using parameter-efficient fine-tuning (PEFT), distillation, and quantization to meet production constraints such as latency, memory, and cost.

  • Apply reinforcement learning and preference optimization to improve personalization and dialogue policies.

  • Scale LLM systems through caching, batching, prompt governance, and evaluation frameworks.

  • Implement privacy, safety, and security controls including PCI compliance, jailbreak resistance, and auditability.

  • Design rigorous experiments with strong baselines and meaningful metrics.

  • Define and track success metrics for agent performance, including task completion rate, accuracy, latency, and customer satisfaction.

Required Qualifications, Capabilities, and Skills

  • Ph.D. with 1+ years or M.S. with 3+ years building and deploying AI systems in production

  • Applied GenAI experience with LLMs including fine-tuning, prompt engineering, and RAG.

  • Experience scaling LLM systems with caching, batching, governance, and evaluation.

  • Strong foundation in ML, deep learning, statistical modeling, and experimental design.

  • Experience in Information Retrieval (indexing, ranking, retrieval) and/or recommendation systems.

  • Proficiency in Python and ML frameworks (PyTorch/TensorFlow, Hugging Face, scikit-learn)

  • Demonstrated ability to set a technical research agenda and drive it from concept through production deployment.

  • Experience presenting research findings and technical strategy to senior leadership and non-technical stakeholders.

Preferred Qualifications, Capabilities, and Skills

  • 5+ years developing conversational AI systems, virtual assistants or LLM-based systems in production.

  • Experience with multi-agent orchestration, supervisor agents, and specialized toolkits.

  • Expertise in agent governance, red-teaming, adversarial testing, and safety evaluation.

  • Experience with reinforcement learning, bandit algorithms, and preference-based optimization (DPO, IPO), with practical exposure to data collection, labeling, and evaluation pipelines.

  • MLOps/LLMOps experience with CI/CD, monitoring, versioning, A/B testing, and rollbacks.

  • Track record of data-driven product development and experimentation.

  • Publications in top-tier AI/ML venues and/or open-source contributions

JPMorganChase, one of the oldest financial institutions, offers innovative financial solutions to millions of consumers, small businesses and many of the world's most prominent corporate, institutional and government clients under the J.P. Morgan and Chase brands. Our history spans over 200 years and today we are a leader in investment banking, consumer and small business banking, commercial banking, financial transaction processing and asset management.

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.

JPMorgan Chase & Co. is an Equal Opportunity Employer, including Disability/Veterans

Our Consumer & Community Banking Group depends on innovators like you to serve consumers, small businesses, municipalities and non-profits.  You'll support the delivery of award winning tools and services that cover everything from personal and small business banking as well as lending, mortgages, credit cards, payments, auto finance and investment advice. This group is also focused on developing and delivering cutting edged mobile applications, digital experiences and next generation banking technology solutions to better serve our clients and customers.

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