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Machine Learning Engineer Quantization Jobs in Auburndale, FL

Techvilla Solutions

Data Scientist

Windermere, FL · On-site

Strong programming experience with Python. * Strong SQL skills for data extraction, transformation, and analysis. * Experience developing and deploying Machine Learning models. * Experience with ...

Publix

Marketing Analytics Data Scientist

Lakeland, FL · On-site

$100 - $125/hr

... science and machine learning solutions that drive business performance, enhance customer ... Ph.D. degree in data science, computer science, statistics, neuroscience, engineering, mathematics ...

Disney Experiences

Senior AI / Data Engineer

Celebration, FL

$93K - $127K/yr

The Data Engineer III role, will report to the Senior Manager, Data Services. About The Role & Team ... machine learning * Uphold design standards and quality assurance protocols for the development of ...

Hcfl

Financial Technology (Adjunct) Faculty

Brandon, FL · On-site

$78/hr

... Programming, Computer Science, Data Analytics, Data Science, Database Design, Finance, Information Security, Information Systems, Machine Learning, Management Information systems, Network ...

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All JobsMachine Learning Engineer Quantization JobsMachine Learning Engineer Quantization Jobs in Auburndale, FL

Machine Learning Engineer Quantization information

See Auburndale, FL salary details

$26.9K

$109.9K

$165.1K

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

As of Sep 8, 2026, the average yearly pay for machine learning engineer quantization in Auburndale, FL is $109,863.00, according to ZipRecruiter salary data. Most workers in this role earn between $86,600.00 and $132,200.00 per year, depending on experience, location, and employer.

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 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 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 job categories do people searching Machine Learning Engineer Quantization jobs in Auburndale, FL look for?

The top searched job categories for Machine Learning Engineer Quantization jobs in Auburndale, FL are:

What cities near Auburndale, FL are hiring for Machine Learning Engineer Quantization jobs?

Cities near Auburndale, FL with the most Machine Learning Engineer Quantization job openings:

Machine Learning Engineer Quantization jobs near you

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

Via Logic LLC

Fort Meade, FL • On-site

$154K - $278K/yr

Full-time

Posted 20 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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