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Machine Learning Engineer Quantization Jobs in Mount Royal, NJ

Merican

AI / Machine Learning Engineer

PA · On-site

AI / Machine Learning Engineer (Contract) Location: Philadelphia, PA or Charlotte, NC Duration: 6 Months Contract Job Summary We are seeking an experienced AI / Machine Learning Engineer to design ...

Bespoke Labs

Machine Learning Engineer

Wilmington, DE · On-site

Must-Have Skills 3+ years of ML engineering experience -- model training, fine-tuning, or post-training pipelines in research or production Strong Python and deep learning proficiency (PyTorch ...

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All JobsMachine Learning Engineer Quantization JobsMachine Learning Engineer Quantization Jobs in Mount Royal, NJ

Machine Learning Engineer Quantization information

See Mount Royal, NJ salary details

$30K

$122.8K

$184.5K

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

As of Jul 14, 2026, the average yearly pay for machine learning engineer quantization in Mount Royal, NJ is $122,775.00, according to ZipRecruiter salary data. Most workers in this role earn between $96,800.00 and $147,800.00 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 cities near Mount Royal, NJ are hiring for Machine Learning Engineer Quantization jobs? Cities near Mount Royal, NJ with the most Machine Learning Engineer Quantization job openings:
Machine Learning Engineer Quantization jobs near you

Machine Learning Engineer (Inference Optimization)

DEEPREC.AI

Philadelphia, PA • On-site

$250K - $450K/yr

Full-time

Posted 10 days ago

Job description

Machine Learning Engineer - Inference Optimization
Overview
We are looking for a Machine Learning Engineer focused on low-latency inference optimization to help build, tune, and productionize high-performance model serving systems. This role sits at the intersection of machine learning, systems engineering, and GPU performance. You will work on inference workloads where latency, throughput, reliability, and hardware efficiency all matter, and where a deep understanding of modern inference runtimes can meaningfully improve production outcomes.
You will work closely with researchers and engineers to understand model structure, identify inference bottlenecks, and turn research ideas into efficient production systems. The work may involve other types of models, but focuses on transformer-style architectures and structured inference workloads. You will evaluate and tune frameworks and related serving or compilation systems, while also reasoning about GPU execution, memory layout, batching strategies, precision tradeoffs, and end-to-end latency.
What you'll do:
  • Design, build, and optimize low-latency inference systems for production machine learning workloads.
  • Profile model inference pipelines across model execution, runtime configuration, batching, memory movement, serialization, networking, and I/O.
  • Evaluate, integrate, and tune inference runtime systems.
  • Improve latency, throughput, and GPU utilization for production inference workloads.
  • Build and support benchmarking and profiling tools to compare model variants, hardware targets, runtime configurations, and deployment strategies.
  • Debug performance issues involving GPU memory, compute saturation, kernel behavior, CPU/GPU coordination, data movement, and serving-layer overhead.
  • Help shape model and system design choices so that research models are efficient to deploy under real latency constraints.
  • Where necessary, collaborate with lower-level systems or GPU specialists on custom operators, kernel-level optimization, or hardware-specific performance work.
What we're looking for:
  • Experience deploying, optimizing, or operating machine learning inference workloads in production or production-like environments.
  • Programming experience in Python, Java, C# etc. and at least one systems language such as C, C , Rust, or Go.
  • Solid understanding of modern ML frameworks such as PyTorch, including model execution, export, tracing, compilation, and performance profiling.
  • Ability to reason about latency, throughput, batching, memory use, GPU utilization, and reliability under real workloads.
  • Strong practical judgment around tradeoffs between model quality, latency, throughput, implementation complexity, and maintainability.
Preferred qualifications:
  • Experience optimizing inference for latency-sensitive or high-throughput applications.
  • Experience with model optimization techniques such as quantization, pruning, distillation, operator fusion, graph lowering, custom operators, or model compilation.
  • Exposure to CUDA, Triton language, ROCm, PTX, CuTe, CUTLASS, FlashInfer, or similar low-level GPU programming tools.
  • Experience running inference workloads on Kubernetes or GPU clusters, including scheduling, autoscaling, observability, and resource management.
  • Background in mathematics, physics, computer science, engineering, statistics, or another technical field.
  • Demonstrated ability to improve real-world inference performance beyond a baseline framework implementation.

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