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Deep Learning Quantization Jobs in Maryland (NOW HIRING)

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Deep Learning Quantization information

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.

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What cities in Maryland are hiring for Deep Learning Quantization jobs?

Cities in Maryland with the most Deep Learning Quantization job openings:

Deep Learning Quantization jobs near you
Infographic showing various Deep Learning Quantization job openings in Maryland as of August 2026, with employment types broken down into 100% Full Time. Highlights an 100% In-person job distribution.

Senior Principal Data Scientist / AI-ML SME (Analytic Superiority)

Via Logic LLC

Fort George G Meade, MD • On-site

$154.05 - $278.48/hr

Other

Posted 15 days ago

Job description

Mission Overview

The Leidos Intel Sector is looking for a premier AI/ML Subject Matter Expert (SME) to serve as a Technical Closer for our COSS 3.0 program supporting USCYBERCOM and the Cyber National Mission Force (CNMF) at Fort Meade, MD. In this elite role, you will architect and engineer the cross‑platform AI frameworks required to achieve absolute analytic superiority.

You will compress both defensive cyber operations (identifying network vulnerabilities and gaps) and offensive operations (vulnerability discovery and automated targeting) from days down to minutes. As a Technical Closer, you will mentor senior technologists by example-working "fingers-on-keyboard" to solve the command's most complex technical roadblocks and push production‑grade AI directly into multi‑cloud, hybrid, and air‑gapped mission enclaves.

Core Technical Requirements
  • Platform‑Agnostic Infrastructure & MLOps: Architect, deploy, and scale distributed AI workloads across any environment required, including AWS SageMaker, Google Vertex AI, Azure Government, and bare‑metal, air‑gapped server racks.
  • Agentic AI & Cyber Automation: Deploy and optimize tools like LangGraph, CrewAI, or AutoGPT to automate cyber threat identification and offensive target generation at wire speed.
  • Low‑Level Model Engineering & Optimization: Fine‑tune open‑source large language models (e.g., Llama 3, Mistral) inside secure enclaves using PyTorch or TensorFlow. Utilize NVIDIA TensorRT, Triton Inference Server, vLLM, and quantization libraries (bitsandbytes) to compress models for high‑throughput execution under strict hardware constraints.
  • Advanced RAG Architectures: Direct the engineering of enterprise Retrieval‑Augmented Generation (RAG) stacks using LangChain paired with high‑performance vector databases like Milvus, Qdrant, or Pinecone.
  • Autonomous Cyber Integration: Connect intelligent agents directly into security orchestration platforms (e.g., Palo Alto Cortex XSIAM/XSOAR) to trigger automated network defense actions and ingest massive, real‑time PCAP and telemetry streams via Apache Kafka/Spark.
  • Polyglot Engineering: Demonstrate engineering mastery in Python, Go, Rust, and C/C++ to build ultra‑fast cyber tools, write optimized GPU kernels, and interface with distributed frameworks like Ray.
Mission & Domain Expertise
  • Dual‑Spectrum Operations: Proven capability to support both Defensive Cyber Operations (DCO) (log parsing, behavioral threat hunting, anomaly detection) and Offensive Cyber Operations (OCO) (automated vulnerability discovery, exploit generation, payload optimization).
  • Mission Platform Orchestration: Experience integrating custom AI/ML pipelines into unified mission systems and high‑value data streams found across Project Maven, Palantir Foundry, and tactical command frameworks.
Required Experience & Background
  • Total Technical Experience: 15+ years of hands‑on experience in software engineering, data science, or distributed systems.
  • Core AI/ML Focus: 5+ years of specialized experience in Machine Learning Engineering, deep learning, or LLM optimization.
  • DoD/IC Ecosystem: 3-5 years working within the DoD/IC cyber ecosystem, specifically building tools that map vulnerabilities or accelerate targeting cycles.
  • Clearance: Active TS/SCI with Polygraph.
  • Work Location: On‑site at Fort Meade, MD (SCIF environment).
Preferred Certifications & Military Equivalency
  • Industry Certifications: Google Cloud Professional Machine Learning Engineer, AWS Certified Machine Learning - Specialty, or NVIDIA Generative AI/LLM Associate.
  • Cyber Mission Force (CMF) Equivalency: Prior certification as a CMF Exploitation Analyst (EA), Digital Network Analyst (DNA), or specialized technical experience as an Army 17A/170A, Navy 181X, or Air Force 17D/17S.
Educational Background
  • Primary Requirement: Master's Degree or PhD in Data Science, Artificial Intelligence, Computer Science, Mathematics, or a related quantitative field. Additional years of experience may be considered in lieu of a degree.
Pay Range

Pay Range $154,050.00 - $278,475.00

Commitment to Non-Discrimination

All qualified applicants will receive consideration for employment without regard to sex, race, ethnicity, age, national origin, citizenship, religion, physical or mental disability, medical condition, genetic information, pregnancy, family structure, marital status, ancestry, domestic partner status, sexual orientation, gender identity or expression, veteran or military status, or any other basis prohibited by law. Leidos will also consider for employment qualified applicants with criminal histories consistent with relevant laws.

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