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Machine Learning Engineer Quantization Jobs in Hyattsville, MD

Machine Learning Engineer We're seeking a skilled Machine Learning Engineer to build and deploy ... Experience with model compression techniques (quantization, pruning, distillation) * Contributions ...

We're seeking a skilled Machine Learning Engineer to build and deploy production ML systems for the ... Experience with model compression techniques (quantization, pruning, distillation) * Contributions ...

Machine Learning Engineer Location: Fort Meade, MD Required Clearance : TS/SCI w/ Full-Scope Poly Salary: Competitive We are seeking a highly skilled and motivated Machine Learning Engineer to join ...

GCP/AWS Machine Learning Engineer Freddie Mac iLab is currently looking for Machine Learning Engineers in its Innovation Labs - Tech Strategy team. In this position, you will be responsible for ...

Machine Learning Engineer Our client, a financial company, is looking for a Machine Learning Engineer for their McLean, VA location. Requirements: * Python, AWS, Kubernetes, Kubeflow, MLOps, ML ...

Machine Learning Engineer Washington, DC (Hybrid) About the Role: We are seeking a highly skilled Machine Learning Engineer to join our core AI team. In this role, you will focus on deploying ...

The Machine Learning Engineer will leverage their strong technical background and knowledge to support highly scalable machine learning-based applications, including both pipelines and services ...

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Machine Learning Engineer Quantization information

See Hyattsville, MD salary details

$31.6K

$129.1K

$193.9K

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 Hyattsville, MD is $129,067.00, according to ZipRecruiter salary data. Most workers in this role earn between $101,700.00 and $155,400.00 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 Hyattsville, MD? For Machine Learning Engineer Quantization jobs in Hyattsville, MD, the most frequently searched job titles are:
What job categories do people searching Machine Learning Engineer Quantization jobs in Hyattsville, MD look for? The top searched job categories for Machine Learning Engineer Quantization jobs in Hyattsville, MD are:
What cities near Hyattsville, MD are hiring for Machine Learning Engineer Quantization jobs? Cities near Hyattsville, MD with the most Machine Learning Engineer Quantization job openings:
Machine Learning Engineer

Machine Learning Engineer

Spear AI

Washington, DC โ€ข On-site, Remote

Other

Medical, Dental, Vision, Life, Retirement, PTO

Posted 23 days ago


Job description

Machine Learning Engineer

We're seeking a skilled Machine Learning Engineer to build and deploy production ML systems for the next-generation data management and artificial intelligence platform for maritime domain awareness.

Spear AI is a growing defense contracting company dedicated to delivering cutting-edge solutions that support our nation's security. As we expand, we're building a culture where innovation meets mission-critical work. We operate with a flat organizational structure that empowers every team member to make an impact, collaborate directly with leadership, and contribute to projects that matter. Whether you're joining our Hardware, Software, or Services division, you'll work alongside talented professionals who are committed to excellence and advancing the capabilities that keep our nation safe and secure.

Spear AI builds sonobuoy sensors that are deployed into the water and collect edge data. We also work with the U.S. Navy to collect and process their SONAR data. You'll have an opportunity to work on real-world projects that directly impact warfighter capabilities and mission success.

What You'll Do
  • Design, train, and optimize machine learning models using PyTorch
  • Deploy models to production environments in the cloud and at the edge
  • Build and maintain ML pipelines for training, evaluation, and inference
  • Integrate machine learning models into real-time and batch processing systems
  • Optimize model performance for accuracy, latency, and resource constraints
  • Implement model monitoring, versioning, and deployment strategies
  • Work with signal processing data and time-series analysis
  • Improve local development and CI/CD for ML workflows using modern tooling and GitHub Actions
Who You Are
  • We're looking for someone with strong Machine Learning Engineering skills who shares our most important values:
  • You're fanatical about polish. Every detail matters. You love to make sure your code is linted, formatted, fully typed, and has comprehensive test coverage.
  • You care about correctness. You take pride in the fact that your models perform reliably and downstream consumers trust your predictions.
  • You obsess over performance. You daydream about model latency, throughput, and efficient inference pipelines.
  • You dive deep. It's important for you to really know how things work. You're always building prototypes and setting up experiments to reinforce your understanding.
  • You live on the bleeding edge. You've got a long list of upcoming ML techniques and frameworks you're excited about and can't wait to experiment with new approaches.
  • You're a great teacher. You know how to break down complex ML concepts for a specific audience and make it click with them in a way that gets them excited.
Why Work With Us
  • We ship โ€” We don't work on 18-month projects that are irrelevant before they're even finished.
  • Our work has impact โ€” We build products that are deployed to U.S. submarines and integrate with the sonobuoys we manufacture.
  • We're growing responsibly โ€” We have the resources to hire a lot more people, but we don't want to build a massive team of people who don't share our values.
  • We're remote โ€” Work from wherever you want. We collaborate in real time on Slack or asynchronously via GitHub.
  • We're profitable โ€” We aren't burning through cash trying to make the business work. But we also have investors who believe in us and are committed to our success.
  • We care about doing great work โ€” You don't need permission to sweat the details here.
  • We don't take ourselves too seriously โ€” We're building products that make the world safer. But we don't let that get to our heads.
Important Skills
  • Several years of experience with Python and machine learning frameworks
  • Expertise in PyTorch for building and training neural networks
  • Experience training and serving models in cloud environments (AWS, Azure, GCP)
  • Proficiency with MLOps practices including experiment tracking, model versioning, and deployment
  • Experience with model optimization for production performance and scale
  • Knowledge of Docker and Kubernetes for containerized deployments
  • Familiarity with REST APIs and model serving frameworks
  • Understanding of CI/CD pipelines for ML systems
  • Strong fundamentals in machine learning including model architecture design, training strategies, and evaluation
Nice To Have
  • Experience with reinforcement learning algorithms and applications
  • Digital signal processing experience
  • Background in time-series analysis or sensor data processing
  • Experience with edge deployment and model optimization for resource-constrained environments
  • Familiarity with distributed training across multiple GPUs/nodes
  • Experience with model compression techniques (quantization, pruning, distillation)
  • Contributions to open-source ML projects or research publications
  • Experience in defense, aerospace, or other regulated industries
What We Offer
  • Unlimited PTO โ€” Take the time you need to recharge and maintain work-life balance.
  • Dedicated Sick Time โ€” Your health and well-being come first.
  • Comprehensive Health & Benefits โ€” Medical, dental, and vision coverage to keep you and your family protected.
  • 11 Paid Holidays โ€” Enjoy time off throughout the year to celebrate and spend with loved ones.
  • Professional Development โ€” Educational opportunities and resources to help you grow your skills and advance your career.
  • Collaborative Environment โ€” Work directly with leadership in our flat organizational structure, where your ideas and contributions matter.
  • Mission-Driven Work โ€” Contribute to projects that directly support national security and make a real-world impact.
  • Growth Opportunities โ€” Join us during an exciting expansion phase where you can help shape our future.
Additional Benefit Opportunities When You Choose Spear AI:
  • 401(k) with company match
  • Onsite / Remote / Flexible work arrangements or hybrid options (position dependent)
  • Relocation assistance (position dependent)
  • Referral bonuses
  • Performance bonuses
  • Life insurance and disability coverage
  • Technology home office setup stipend
  • Professional certification reimbursement (position dependent)

We offer competitive compensation tailored to your experience, location, and the impact you'll make. We're committed to equitable pay and will share a range aligned to your level and geography during the hiring process. In accordance with state law, candidates in jurisdictions such as CA, CO, WA, NY, and others, where applicable, will be provided a good-faith salary range upon request and through the hiring process. This is a full-time, exempt position under the Fair Labor Standards Act (FLSA) and is not eligible for overtime pay.

Compensation for this position is provided on a salaried basis and is not subject to reduction based on hours worked. At Spear AI, you'll find more than just a job; you'll join a mission-driven team where your work directly contributes to national security. Our flat organizational structure means your voice matters, your ideas reach leadership, and your impact is visible. As we grow, we're committed to building robust processes and infrastructure that support both our mission and our people. We value collaboration, continuous improvement, and the expertise each team member brings to the table. If you're looking for a place to grow professionally while working on projects that truly matter, we'd love to hear from you.

You must be willing to receive a Secret or Top Secret/SCI security clearance. This will be at no expense to you. For resources on what goes into a security background investigation and what disqualifies people reference the CIA requirements.


Spear AI logo

About Spear AI

Sourced by ZipRecruiter

Industry

Guided missile and space vehicle manufacturing

Company size

11 - 50 Employees

Headquarters location

Washington, DC, US

Year founded

2020