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Deep Learning Quantization Jobs in Fairfax, VA (NOW HIRING)

Entarian

MLOps Engineer

Arlington, VA

Deploy and manage machine learning models in production using tools like MLflow, Kubeflow, or AWS ... Optimize models for production (e.g., via quantization or pruning) and ensure efficient resource ...

Entarian

MLOps Engineer

Arlington, VA · On-site

Deploy and manage machine learning models in production using tools like MLflow, Kubeflow, or AWS ... Optimize models for production (e.g., via quantization or pruning) and ensure efficient resource ...

Entarian

MLOps Engineer

Arlington, VA

Deploy and manage machine learning models in production using tools like MLflow, Kubeflow, or AWS ... Optimize models for production (e.g., via quantization or pruning) and ensure efficient resource ...

Entarian

MLOps Engineer

Arlington, VA

Deploy and manage machine learning models in production using tools like MLflow, Kubeflow, or AWS ... Optimize models for production (e.g., via quantization or pruning) and ensure efficient resource ...

Entarian

MLOps Engineer

Arlington, VA · On-site

Deploy and manage machine learning models in production using tools like MLflow, Kubeflow, or AWS ... Optimize models for production (e.g., via quantization or pruning) and ensure efficient resource ...

Entarian

MLOps Engineer

Arlington, VA · On-site

Deploy and manage machine learning models in production using tools like MLflow, Kubeflow, or AWS ... Optimize models for production (e.g., via quantization or pruning) and ensure efficient resource ...

Entarian

MLOps Engineer

Arlington, VA · On-site

Deploy and manage machine learning models in production using tools like MLflow, Kubeflow, or AWS ... Optimize models for production (e.g., via quantization or pruning) and ensure efficient resource ...

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Showing results 41-50

All JobsDeep Learning Quantization JobsDeep Learning Quantization Jobs in Fairfax, VA

Deep Learning Quantization information

See Fairfax, VA salary details

$11.2K

$85.7K

$143.1K

How much do deep learning quantization jobs pay per year?

As of Aug 19, 2026, the average yearly pay for deep learning quantization in Fairfax, VA is $85,745.00, according to ZipRecruiter salary data. Most workers in this role earn between $73,600.00 and $142,100.00 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 Fairfax, VA?

For Deep Learning Quantization jobs in Fairfax, VA, the most frequently searched job titles are:

What job categories do people searching Deep Learning Quantization jobs in Fairfax, VA look for?

The top searched job categories for Deep Learning Quantization jobs in Fairfax, VA are:

What cities near Fairfax, VA are hiring for Deep Learning Quantization jobs?

Cities near Fairfax, VA with the most Deep Learning Quantization job openings:

Deep Learning Quantization jobs near you
Infographic showing various Deep Learning Quantization job openings in Fairfax, VA as of August 2026, with employment types broken down into 1% As Needed, 73% Full Time, 23% Part Time, 1% Temporary, and 2% Contract. Highlights an 87% Physical, 2% Hybrid, and 11% Remote job distribution, with an average salary of $85,745 per year, or $41.2 per hour.

AI Lead Software Architect - SME

Dark Wolf Solutions

Herndon, VA • On-site

$225K - $285K/yr

Full-time

Re-posted 23 days ago

Job description

Dark Wolf constructs and deploys data management and analytics solutions for the defense and intelligence communities. We're proud to boast a world-class engineering team that thrives on rolling up their sleeves to solve your mission's biggest challenges.
Dark Wolf is seeking an elite AI Software Architect (SME) to drive the vision, structural topology, and enterprise-wide architectural execution of mission-critical Artificial Intelligence and Machine Learning systems. Operating at the apex of software engineering and hardware acceleration, you will design fault-tolerant, resilient, and ultra-low-latency distributed AI architectures deployed across air-gapped, multi-cloud, and tactical edge environments.
In this role, you will serve as the principal technical authority, bridging high-assurance systems engineering with bleeding-edge AI models, custom inference engines, multi-agent frameworks, and zero-trust DevSecOps pipelines.
Key Responsibilities
  • Enterprise AI System Design: Architect end-to-end distributed AI platforms, high-throughput model inference pipelines, and scalable enterprise LLM/SLM deployment topologies tailored for classified enclaves.
  • Autonomous & Agentic Systems: Design resilient multi-agent orchestration engines, continuous Retrieval-Augmented Generation (RAG) platforms, and real-time semantic routing layers using modern framework paradigms.
  • Hardware & Inference Optimization: Lead system trade studies to optimize compute across heterogeneous hardware (GPUs, TPUs, NPUs), implementing advanced quantization, speculative decoding, and custom execution kernels for edge and air-gapped environments.
  • Zero-Trust Security & Governance: Establish system-wide AI security postures, incorporating automated DevSecOps, prompt-injection guardrails, differential privacy, and rigorous supply-chain risk management for ML artifacts.
  • Technical Authority & Roadmap Strategy: Interface directly with executive leadership and intelligence community stakeholders to map mission objectives to technical architectures, establish enterprise coding and safety standards, and direct R&D initiatives.

Required Qualifications:
  • A Bachelor's degree in Computer Science, Information Systems, Engineering, or a related technical field (Master's degree or Ph.D. strongly preferred).
  • 15+ years of software engineering and systems architecture experience, with demonstrated leadership in delivering enterprise-scale AI/ML solutions.
  • AI Frameworks & LLMOps: Advanced mastery of low-level framework mechanics (PyTorch, TensorRT-LLM, vLLM, DeepSpeed, Ray), custom extension development, and enterprise orchestration platforms (LangGraph, AutoGen, LlamaIndex).
  • AI Models & Fine-Tuning Strategy: Expertise in novel architecture adaptation, speculative decoding, mixture-of-experts (MoE), parameter-efficient fine-tuning (LoRA, QLoRA), and post-training alignment (RLHF, DPO, GRPO).
  • Machine Learning Systems Engineering: MLOps/LLMOps architecture, model governance, continuous training pipelines, real-time drift detection, and deterministic evaluation frameworks.
  • Systems Programming & Performance: Polyglot mastery in Java, Rust, Python, and C, with deep expertise in asynchronous execution, memory management, CUDA/Triton kernels, and hardware-level performance profiling.
  • Containerization & Mesh Orchestration: Enterprise Kubernetes multi-cluster federation, custom CRDs, Service Mesh (Istio), bare-metal GPU scheduling, and zero-trust containerization strategies.
  • Multi-Cloud & Air-Gapped Infrastructure: Cross-cloud architecture (AWS GovCloud, Azure Secret), Infrastructure as Code (Terraform, Pulumi), and disconnected/air-gapped tactical node deployment methodologies.
  • DevSecOps & AI Security Tooling: Designing automated SAST/DAST pipelines, confidential computing enclaves (TEEs), runtime guardrails, adversarial AI defense, and automated vulnerability remediation frameworks.
  • Agile & Enterprise Transformation: Steering multi-pod engineering teams, establishing SAFe/Scaled Agile systems execution, and managing architectural debt across multi-year programs.

Desired Qualifications:
  • Advanced Certifications: AWS Certified Solutions Architect - Professional, Certified Information Systems Security Professional (CISSP), Certified Kubernetes Administrator (CKA), or specialized High-Performance Computing (HPC) credentials.
  • Pioneering Field Work: Proven track record architecting and deploying petabyte-scale ML systems or multi-agent autonomous frameworks into classified, air-gapped intelligence networks.
  • Recognized technical leadership in the broader AI engineering community (e.g., open-source contributions, technical publications, or patent holdings in distributed AI systems/architectures).

Position Clearance Requirement:
US Citizenship with an active TS/SCI security clearance with Full-Scope Polygraph
Location: Chantilly/Herndon, VA.
Target Salary Range: $225,000.00 - $285,000.00+ (Commensurate with specialized architecture expertise and technical skillset)
Equal Opportunity Employer:
We are proud to be an EEO/AA employer Minorities/Women/Veterans/Disabled and other protected categories.
In compliance with federal law, all persons hired will be required to verify identity and eligibility to work in the United States and to complete the required employment eligibility verification form upon hire.
We are strictly looking for direct, full-time W2 employees. We do not engage with third-party staffing agencies, C2C, or 1099 independent contractors for this role.

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