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Deep Learning Quantization Jobs in Washington, DC

GRVTY

Expert ML Software Engineer

Springfield, VA

Create and train ML models for classification, regression, forecasting, or deep learning * Pipeline ... and batching/quantization tradeoffs * Experience with GPU orchestration and scheduling on ...

Twenty Technologies, Inc

Applied AI Engineer

Arlington, VA · On-site

$159K - $263K/yr

You have strong Python skills and deep learning experience with PyTorch, TensorFlow, or JAX. * You ... You have implemented quantization or other optimization techniques to improve inference efficiency.

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All JobsDeep Learning Quantization JobsDeep Learning Quantization Jobs in Washington, DC

Deep Learning Quantization information

See Washington, DC salary details

$12.5K

$95K

$158.6K

How much do deep learning quantization jobs pay per year?

As of Aug 28, 2026, the average yearly pay for deep learning quantization in Washington, DC is $95,008.00, according to ZipRecruiter salary data. Most workers in this role earn between $81,500.00 and $157,400.00 per year, depending on experience, location, and employer.

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 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 are popular job titles related to Deep Learning Quantization jobs in Washington, DC?

For Deep Learning Quantization jobs in Washington, DC, the most frequently searched job titles are:

What job categories do people searching Deep Learning Quantization jobs in Washington, DC look for?

The top searched job categories for Deep Learning Quantization jobs in Washington, DC are:

Deep Learning Quantization jobs near you

Senior Wireless Machine Learning Engineer, AI-RAN

Arlington, VA • On-site

DeepSig Inc Careers Page
See more jobs at DeepSig Inc Careers Pageexternal

Full-time

Posted yesterday

New

Job description

Description:

Type: Full-Time(W2) On-site/Hybrid, Arlington, VA (Remote option available for the right candidate)


DeepSig is defining the future of wireless communications by merging deep learning with the Radio Access Network (RAN). We are seeking an experienced Technical Lead to architect and drive the development of our next-generation AI-native RAN.


In this role, you will design, prototype, and validate novel AI/ML components—such as neural receivers, neural beamforming, neural scheduling, digital twin, and ISAC (Integrated Sensing and Communications)—that outperform traditional signal processing methods. You will work at the cutting edge of 6G innovation, taking concepts from mathematical intuition to simulation (e.g. NVIDIA Sionna) and real-time implementation.


What You’ll be Doing 

  • Applied AI Research: Design and train modern deep learning models (Transformers, Vision architectures, etc.) to solve complex physical layer problems, including channel estimation, MIMO detection, and beam management
  • Simulation & Validation: Build high-fidelity link-level simulations using NVIDIA Sionna and ray-tracing to train, test, and benchmark AI models against legacy 5G baselines
  • Prototyping & Deployment: Transition research models into deployable "dApps" for the Distributed Unit (DU), optimizing inference for latency and compute efficiency on NVIDIA GPUs
  • New Capabilities: Explore emerging AI-RAN frontiers such as Integrated Sensing and Communications (ISAC), neural scheduling, and channel digital twins
  • Innovation & IPR: Drive technical innovation by authoring invention disclosures, filing patents, and generating technical reports to support our standardization team in 3GPP and O-RAN Alliance contributions
  • Data Engineering: Architect data pipelines for generating synthetic training datasets and developing "Sim-to-Real" transfer techniques to ensure robust performance in real-world networks

Required Qualifications

  • Education: Ph.D. or Master’s in Computer Science, Electrical Engineering, or Applied Mathematics with a focus on Deep Learning and/or Communications Systems
  • AI/ML Expertise: 3+ years of experience designing and training deep neural networks from scratch. Strong grasp of modern architectures and optimization techniques
  • Applied Signal Processing: Experience applying machine learning to real-time time-series data, signal processing, or physics-based problems (Audio, RF, or similar domains)
  • Research to Code: Proven ability to read academic papers and implement their methods in robust Python code
  • Simulation Skills: Experience with differentiable simulation or digital twins (e.g., Sionna, JAX-based physics sims)

Preferred Qualifications

  • Wireless Knowledge: Understanding of wireless fundamentals (OFDM, MIMO, IQ data) is highly helpful, though we prioritize strong ML intuition over pure communication theory
  • Performance Optimization: Experience with model quantization (FP16/INT8), pruning, or using TensorRT for real-time inference
  • Standardization Support: Experience writing technical whitepapers or supporting patent filings in a research environment
  • C++ Integration: Ability to write C++ bindings or integrate Python models into C++, SIMD, and Cuda production pipelines

Working at DeepSig

DeepSig is growing its technical team while cultivating a collaborative, agile, and fun small-team culture. We value creativity, knowledge sharing, and employee growth, and we encourage participation in scientific publications, conferences, and open-source software. We offer competitive salaries and benefits, an employee stock option grant program, an environment where we are excited to be transforming and disrupting how signal processing is done with AI/ML, a welcoming and inclusive environment, a flexible schedule, and a great work / life balance.


DeepSig is an equal-opportunity employer and does not discriminate based on race, ethnicity, religion, color, national origin, gender, sexual orientation, age, marital status, veteran status, or disability. We are dedicated to cultivating an inclusive, diverse, and engaging workplace where individuals feel fulfilled, inspired, and motivated. We value the unique perspectives that our team brings.





Requirements: