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Machine Learning Engineer Quantization Jobs in Baltimore, MD

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 ...

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 ...

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 ...

Machine Learning Engineer

Jessup, MD · On-site

$100K - $137K/yr

Worker Type Regular AV is seeking a Software Engineer 3 with Machine Learning (ML) & Artificial Intelligence (AI) experience, for our PRIME contract. The ideal candidate will be responsible for ...

Senior Machine Learning Engineer

Silver Spring, MD · Hybrid

$108K - $148K/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 ...

Worker Type Regular AV is seeking aSoftware Engineer 3with Machine Learning (ML) & Artificial Intelligence (AI) experience,for our PRIMEcontract.The ideal candidate willbe responsible fordesigning ...

... Engineer 3with Machine Learning (ML) & Artificial Intelligence (AI) experience,for our PRIMEcontract.The ideal candidate willbe responsible fordesigning and developing robust software ...

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Machine Learning Engineer Quantization information

See Baltimore, MD salary details

$31.3K

$128K

$192.3K

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

As of Jun 18, 2026, the average yearly pay for machine learning engineer quantization in Baltimore, MD is $127,950.00, according to ZipRecruiter salary data. Most workers in this role earn between $100,900.00 and $154,000.00 per year, depending on experience, location, and employer.

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 Baltimore, MD? For Machine Learning Engineer Quantization jobs in Baltimore, MD, the most frequently searched job titles are:
What job categories do people searching Machine Learning Engineer Quantization jobs in Baltimore, MD look for? The top searched job categories for Machine Learning Engineer Quantization jobs in Baltimore, MD are:
What cities near Baltimore, MD are hiring for Machine Learning Engineer Quantization jobs? Cities near Baltimore, MD with the most Machine Learning Engineer Quantization job openings:
Infographic showing various Machine Learning Engineer Quantization job openings in Baltimore, MD as of June 2026, with employment types broken down into 70% Full Time, and 30% Part Time. Highlights an 86% Physical, 5% Hybrid, and 9% Remote job distribution, with an average salary of $127,950 per year, or $61.5 per hour.
Machine Learning Engineer

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.