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Machine Learning Engineer Quantization Jobs in Maryland

Razor Talent

Machine Learning Engineer

Columbia, MD

$2.0K/mo

Machine Learning Engineer Our clients, a rapidly growing AI-focused software development company supporting federal agencies, is seeking a Machine Learning Engineer. Delivers mission-critical ...

Cymertek

Machine Learning Engineer

Annapolis, MD

Machine Learning Engineer LOCATION Annapolis Junction, MD 20701 CLEARANCE TS/SCI Full Poly (Please note this position requires full U.S. Citizenship) KEY SUMMARY We are seeking a talented and ...

Indeed

Machine Learning Engineer

Annapolis, MD

As a Machine Learning Engineer, you will prepare datasets, train and optimize models, and maintain and improve model inference services. You will learn and apply new techniques from open source ...

Autodesk

Senior Machine Learning Engineer

California, MD · On-site +1

$100K - $137K/yr

Position Overview As a Senior Machine Learning Engineer, you will play a key role in designing, developing, and evolving machine learning systems that support conversational AI, search, multi-agent ...

Xometry

Senior Machine Learning Engineer

Rockville, MD

$106K - $146K/yr

Senior Machine Learning Engineer North Bethesda, MD Xometry powers the industries of today and tomorrow by connecting the people with big ideas to the manufacturers who can bring them to life.

Xometry

Senior Machine Learning Engineer

North Bethesda, MD · Hybrid

$104K - $143K/yr

Xometry is seeking a Senior Machine Learning Engineer to join our growing organization. The right person will help move our machine learning capabilities to the next level. You'll be working in an ...

GEICO

Machine Learning Engineer II

Bethesda, MD · On-site

$105K - $215K/yr

Leverage expertise in machine learning, software engineering, and system architecture to independently drive the development of production-ready models. Work closely with cross-functional teams, from ...

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Machine Learning Engineer Quantization information

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 are popular job titles related to Machine Learning Engineer Quantization jobs in Maryland? For Machine Learning Engineer Quantization jobs in Maryland, the most frequently searched job titles are:
What job categories do people searching Machine Learning Engineer Quantization jobs in Maryland look for? The top searched job categories for Machine Learning Engineer Quantization jobs in Maryland are:
What cities in Maryland are hiring for Machine Learning Engineer Quantization jobs? Cities in Maryland with the most Machine Learning Engineer Quantization job openings:
Machine Learning Engineer Quantization jobs near you
Machine Learning Engineer

Machine Learning Engineer

Maverc Technologies

Columbia, MD

Full-time

Posted 29 days ago

Job description

Become part of a team solving the most significant Cybersecurity & IT Challenges and helping keep the world’s largest and most elite brands safer from cyber threats. At Maverc we have a powerful mindset based on our core values of being accountable, helpful, adaptable, and focused. Maverc Technologies is a proven and effective small business partner and consultant, recognized as a leader in providing cyber security and IT services to the Federal, State, and local Government and within the Intelligence Community. Maverc Technologies is seeking an Machine Learning Engineer to support one of our corporate customers.



Job Duties and Responsibilities 

A talented Machine Learning Engineer to support our AI Center of Excellence! In this role, you and your team will be responsible for the entire lifecycle of machine learning models, from managing and deploying them to troubleshooting any pipeline issues that arise. We offer a collaborative environment where you will work closely with engineers and data scientists to bring impactful ML solutions to life.

Responsibilities include, but are not limited to:

  • Manage and deploy machine learning models into production
  • Debug and troubleshoot issues with deployment pipelines
  • Utilize and understand core ML tooling
  • Work with dataframes to manipulate and prepare data for models
  • Collaborate with the various teams within the AI Center of Excellence to ensure successful model implementation
  • Analyze large amounts of information to discover trends and patterns
  • Build predictive models and machine-learning algorithms


QUALIFICATIONS AND EXPERIENCE 

  • Active SECRET
  • US Citizenship
  • Minimum of 8 years’ experience in DevOps or MLOps
  • Understanding of machine learning modeling techniques and algorithms
  • Experience with Python, Docker, Kubernetes and Git
  • Skilled in common data science libraries (Scikit-learn, PyTorch, etc)
  • Strong math skills (e.g. statistics, algebra)
  • Problem-solving aptitude
  • Excellent communication and presentation skills
  • Experience with deploying open-source LLMs
  • DataBricks
  • Splunk
  • Continuous Integration/Continuous Deployment
  • Knowledge of statistics and concepts in neural networks


Education: Bachelor’s or Master’s in Computer Science, Computer Engineering, or other related field.


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