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Machine Learning Engineer Quantization Jobs in New York

exacare ai

Machine Learning Engineer

New York, NY · On-site

About the Role We are looking for a Machine Learning Engineer, MLOps to help operationalize and scale our machine learning systems. This is an engineering-focused role centered on building the ...

Virtu Financial

Machine Learning Engineer

New York, NY · On-site

$200K - $300K/yr

Virtu's Research Technology team is looking for an experienced Machine Learning Engineer to join a small group of technologists whose primary function is building the infrastructure that powers our ...

Virtu Financial

Machine Learning Engineer

New York, NY · On-site

$200K - $300K/yr

Virtu's Research Technology team is looking for an experienced Machine Learning Engineer to join a small group of technologists whose primary function is building the infrastructure that powers our ...

Root Access Inc

Machine Learning Engineer

New York, NY · On-site

Our team is a passionate mix of engineers across electrical, firmware, software, and machine learning. Core Responsibilities * Architect Physics Foundation Models: Design and train deep learning ...

Capital One

Sr. Lead Machine Learning Engineer

New York, NY · On-site +1

$112K - $147K/yr

Sr. Lead Machine Learning Engineer As a Capital One Machine Learning Engineer (MLE) , you'll be part of an Agile team dedicated to productionizing machine learning applications and systems at scale.

NYULMC

Machine Learning Engineer

New York, NY · On-site

As a ML Engineer, you will support the implementation of diverse Generative AI and Machine Learning initiatives across the health system. You will be responsible for driving specific projects forward ...

SageSure

Senior Machine Learning Engineer

Jersey City, NJ · On-site

$127K - $168K/yr

As a Senior Machine Learning Engineer, you'll play a crucial role in optimizing orchestration processes and ensuring fast and efficient model deployment and delivery. You'll work closely with ...

FINNY

Machine Learning Engineer

Manhattan, NY · On-site

The Machine Learning Engineer will own the models that power various features across the product, collaborating with teams to improve ML systems that shape user outcomes. Responsibilities : • ...

Autodesk

Senior Machine Learning Engineer

New York, NY · On-site +1

$114K - $157K/yr

Position Overview As a Senior Machine Learning Engineer, you will play a key role in designing, developing, and evolving machine learning systems that support conversational AI, search, multi-agent ...

Teleskope

Machine Learning Engineer

New York, NY · On-site

$160K - $210K/yr

About the role We are seeking a Machine Learning Engineer to strengthen our element classification system - working closely with data scientists and data annotators to ship and improve entity ...

TransRe

Machine Learning Engineer

New York, NY · On-site

$145K - $170K/yr

Constructing machine learning models including data collection, normalization, and standardization, data pipeline construction, model selection and hyperparameter tuning, working ml systems that can ...

TransRe

Machine Learning Engineer

New York, NY · Hybrid

$145K - $170K/yr

Constructing machine learning models including data collection, normalization, and standardization, data pipeline construction, model selection and hyperparameter tuning, working ml systems that can ...

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Machine Learning Engineer Quantization information

What are some common challenges Machine Learning Engineers face when implementing quantization techniques in production models?

Machine Learning Engineers working on quantization often encounter challenges such as balancing reduced model size and computational efficiency with maintaining acceptable accuracy levels. Adapting quantization methods to different hardware platforms can also require significant testing and optimization. Additionally, engineers must frequently address compatibility issues with existing deployment pipelines and ensure that quantization-aware training is properly integrated to minimize performance degradation. Collaboration with hardware and software teams is essential to streamline deployment and achieve optimal results.

What are the key skills and qualifications needed to thrive as a Machine Learning Engineer Quantization, and why are they important?

To thrive as a Machine Learning Engineer Quantization, you need a solid background in machine learning, deep learning, and computer science, typically supported by a degree in a related field. Familiarity with quantization techniques, frameworks such as TensorFlow Lite or PyTorch, and experience with hardware accelerators are crucial. Strong problem-solving skills, attention to detail, and effective collaboration set top performers apart. These capabilities are vital for efficiently deploying high-performing models on resource-constrained devices and ensuring scalable, real-world AI solutions.

What does a Machine Learning Engineer Quantization do?

A Machine Learning Engineer specializing in quantization focuses on optimizing machine learning models by reducing their size and computational requirements without significantly sacrificing accuracy. This involves converting model parameters and computations from high-precision formats (like 32-bit floating point) to lower-precision formats (such as 8-bit integers). Quantization enables faster inference, lower memory usage, and allows models to run efficiently on edge devices and mobile platforms. These engineers work closely with data scientists and hardware teams to implement, test, and validate quantized models in production environments.

What is the difference between Machine Learning Engineer Quantization vs Data Scientist?

AspectMachine Learning Engineer QuantizationData Scientist
Required CredentialsBachelor's or master's in CS, ML, or related; certifications in ML or AIBachelor's or master's in statistics, CS, or related; certifications in data analysis or statistics
Work EnvironmentDeveloping optimized ML models, deploying quantized models for efficiencyAnalyzing data, building predictive models, interpreting results
Industry UsageTech companies, AI hardware firms, embedded systemsFinance, healthcare, marketing, research institutions

Machine Learning Engineer Quantization focuses on optimizing ML models for deployment efficiency, often working closely with hardware and software teams. Data Scientists analyze data and build models for insights. While both roles require ML knowledge, quantization engineers specialize in model compression techniques, whereas data scientists focus on data analysis and interpretation.

What are popular job titles related to Machine Learning Engineer Quantization jobs in New York? For Machine Learning Engineer Quantization jobs in New York, the most frequently searched job titles are:
What job categories do people searching Machine Learning Engineer Quantization jobs in New York look for? The top searched job categories for Machine Learning Engineer Quantization jobs in New York are:
What cities in New York are hiring for Machine Learning Engineer Quantization jobs? Cities in New York with the most Machine Learning Engineer Quantization job openings:
Machine Learning Engineer Quantization jobs near you

Machine Learning Engineer

exacare ai

New York, NY • On-site

Full-time

Medical, Dental, Vision, PTO

Re-posted 17 days ago

Job description

About the Role
We are looking for a Machine Learning Engineer, MLOps to help operationalize and scale our machine learning systems. This is an engineering-focused role centered on building the workflows, infrastructure, and processes that enable ML to move from research into reliable production systems.
You will partner closely with research-oriented ML teammates and help turn their work into scalable, maintainable, and cost-effective production systems. This includes building and improving data pipelines, training pipelines, deployment workflows, monitoring systems, and supporting infrastructure that allow the team to move faster and operate ML systems with confidence.
This is not a research-first role. It is best suited for someone who is excited by the systems, tooling, and operational side of machine learning.
What You'll Do
  • Build and maintain the workflows and infrastructure that support the end-to-end ML lifecycle
  • Partner with researchers and ML practitioners to productionize models and enable faster iteration
  • Design, build, and improve data pipelines and training pipelines
  • Improve data processing, annotation workflows, and ML system efficiency
  • Deploy and maintain the background systems that support model training and inference
  • Build tooling and processes for monitoring model performance, system reliability, and operational health
  • Improve the scalability, observability, and reproducibility of ML systems
  • Optimize ML infrastructure for speed, reliability, and cost-efficiency
  • Identify bottlenecks in the ML workflow and automate or streamline manual processes
  • Help establish best practices around ML operations, deployment, and system performance

What You'll Bring
  • Proven (3+ years) of experience in machine learning engineering, MLOps, ML infrastructure, data engineering, or backend/platform engineering in ML environments
  • Experience supporting ML systems end to end, from model handoff through deployment and monitoring
  • Strong experience building and owning data pipelines, training pipelines, or other production workflows that support ML
  • Experience working closely with researchers, data scientists, or ML practitioners to productionize models
  • Strong software engineering fundamentals and experience building production systems
  • Experience with monitoring, debugging, and improving production ML or data systems
  • A track record of improving reliability, scalability, speed, and/or cost efficiency in ML systems
  • Comfort operating in a fast-moving, startup-style environment with a high degree of ownership

Benefits + Perks
  • Competitive salary and equity in a high-growth startup
  • Flexible PTO, take what you need
  • Medical, dental, and vision coverage
  • Great startup culture, including company off-sites
  • High-achieving team, including ex-Amazon engineers and alumni of Bain, BCG, Goldman Sachs, and more

An insight into our Core Values
Only the best belong here
We are unapologetic about talent. This should be the best team you have ever been on. Protecting that standard is how we honor each other's time, ambition, and craft.
We work even harder to keep our partners than we did to earn them initially
The work does not stop when a customer first onboards to our platform. It deepens over time. We partner with operators, listening and learning about real problems, and translate that into solutions that help them succeed in practice. We earn trust through consistent delivery.
We keep the patient downstream of every decision
At the end of the day, this is about the patient. We get there by deeply respecting and reflecting on our purpose: to develop software that aids teams in delivering better care.
Raise the bar on ownership
We grow because people here go beyond the minimum. We invest extra effort, care, and ownership into what we build.
The world is moving fast. We move faster.
This is a race. We work hard, we move early, and we stay ahead of problems and competitors. If we slow down, someone else will pass us.
Radical candor, zero politics
We say what's true, early, and we keep communication direct and clean so the team can move.
Bring good vibes and win together
We win as a team. We bring energy, support each other, and make the workplace somewhere people are excited to show up.
If this sounds like you, we'd love to have a chat!
#LI-Hybrid
About ExaCare AI
ExaCare AI is a leading health tech company on a mission to build the AI operating system for post-acute care. Our platform turns messy, unstructured referral packets into clear clinical insights and next steps, so teams can make faster, safer placement decisions with less administrative burden. Today, ExaCare AI powers more than 2000 facilities, and is growing rapidly.
We recently raised a $30M Series A led by Insight Partners, and are bringing world-class talent together to transform healthcare. If you like building, learning, and want to make a real impact, come join us!

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