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Machine Learning Engineer Quantization Jobs in Bronx, NY

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 ...

RunSybil

Machine Learning Engineer

New York, NY · On-site

About this Role We are seeking talented engineers intent on changing the security industry. If you ... Understanding of both modern and classic machine learning techniques * Equally comfortable with ...

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All JobsMachine Learning Engineer Quantization JobsMachine Learning Engineer Quantization Jobs in Bronx, NY

Machine Learning Engineer Quantization information

See Bronx, NY salary details

$32.8K

$134.1K

$201.6K

How much do machine learning engineer quantization jobs pay per year?

As of Jul 13, 2026, the average yearly pay for machine learning engineer quantization in Bronx, NY is $134,147.00, according to ZipRecruiter salary data. Most workers in this role earn between $105,700.00 and $161,500.00 per year, depending on experience, location, and employer.

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

Machine Learning Engineer

Root Access Inc

New York, NY • On-site

Full-time

Posted 3 days ago

Job description

About the company
Root Access is a frontier electronics company. We are a NYC-based startup funded by top investors. 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 models-specifically PINNs, FNOs, and Neural Operators-optimized to solve Maxwell's equations, Helmholtz equations, and heat equations directly within the neural loss function.
  • Build the ECAD Data Pipeline: Develop high-performance asset pipelines to convert geometric, discrete, and multi-layer PCB files (ODB++, IPC-2581, STEP, Gerber) into continuous tensor grids, signed distance fields (SDFs), or graph embeddings.
  • Close the Simulation-to-Reality (Sim2Real) Gap: Implement Differentiable Physics Calibration pipelines to ingest physical lab measurements (VNA Touchstone files, TDR traces, near-field EMI scans) to fine-tune latent material and manufacturing parameters.
  • Multi-Modal Architecture Integration: Collaborate on connecting upstream Graph Neural Networks (GNNs) or LLMs mapping schematic topologies to downstream spatial physics engines.
  • Optimize for Real-Time Execution: Optimize training and inference pipelines on GPU clusters to ensure forward-pass physics predictions can execute in sub-100 millisecond timeframes, enabling real-time feedback loops for layout designers.

Required Technical Skills & Qualifications
  • Education: Master's or Ph.D. in Computer Science, Mathematics, EE, Physics, or a related quantitative field with a focus on Scientific Machine Learning (SciML).
  • Deep Learning Frameworks: 4+ years of expert-level experience with PyTorch or JAX.
  • SciML Expertise: Direct, hands-on experience building and training PINNs, DeepONets, or Fourier Neural Operators (FNOs). Direct experience using frameworks like NVIDIA Modulus, DeepXDE, or PyTorch Geometric.
  • Mathematical Depth: Exceptional understanding of partial differential equations (PDEs), vector calculus, automatic differentiation (autograd), and numerical optimization algorithms (Adam, L-BFGS).
  • Data Pipelines: Strong proficiency in manipulating spatial or geometric datasets using Python libraries (NumPy, SciPy, Shapely, Open3D, or custom voxelization matrices).

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