Deploy and manage machine learning models in production using tools like MLflow, Kubeflow, or AWS ... Optimize models for production (e.g., via quantization or pruning) and ensure efficient resource ...
Deploy and manage machine learning models in production using tools like MLflow, Kubeflow, or AWS ... Optimize models for production (e.g., via quantization or pruning) and ensure efficient resource ...
Deploy and manage machine learning models in production using tools like MLflow, Kubeflow, or AWS ... Optimize models for production (e.g., via quantization or pruning) and ensure efficient resource ...
Deploy and manage machine learning models in production using tools like MLflow, Kubeflow, or AWS ... Optimize models for production (e.g., via quantization or pruning) and ensure efficient resource ...
MLOps Engineer
Arlington, VA · On-site
Deploy and manage machine learning models in production using tools like MLflow, Kubeflow, or AWS ... Optimize models for production (e.g., via quantization or pruning) and ensure efficient resource ...
MLOps Engineer
Arlington, VA · On-site
Deploy and manage machine learning models in production using tools like MLflow, Kubeflow, or AWS ... Optimize models for production (e.g., via quantization or pruning) and ensure efficient resource ...
MLOps Engineer
Arlington, VA · On-site
Deploy and manage machine learning models in production using tools like MLflow, Kubeflow, or AWS ... Optimize models for production (e.g., via quantization or pruning) and ensure efficient resource ...
MLOps Engineer
Arlington, VA · On-site
Deploy and manage machine learning models in production using tools like MLflow, Kubeflow, or AWS ... Optimize models for production (e.g., via quantization or pruning) and ensure efficient resource ...
Deploy and manage machine learning models in production using tools like MLflow, Kubeflow, or AWS ... Optimize models for production (e.g., via quantization or pruning) and ensure efficient resource ...
Deploy and manage machine learning models in production using tools like MLflow, Kubeflow, or AWS ... Optimize models for production (e.g., via quantization or pruning) and ensure efficient resource ...
Deploy and manage machine learning models in production using tools like MLflow, Kubeflow, or AWS ... Optimize models for production (e.g., via quantization or pruning) and ensure efficient resource ...
Deploy and manage machine learning models in production using tools like MLflow, Kubeflow, or AWS ... Optimize models for production (e.g., via quantization or pruning) and ensure efficient resource ...
Deploy and manage machine learning models in production using tools like MLflow, Kubeflow, or AWS ... Optimize models for production (e.g., via quantization or pruning) and ensure efficient resource ...
Deploy and manage machine learning models in production using tools like MLflow, Kubeflow, or AWS ... Optimize models for production (e.g., via quantization or pruning) and ensure efficient resource ...
Artificial Intelligence Integration Engineer with Security Clearance
$119.80K - $161.30K/yr
Deploy and manage machine learning models in production using tools like MLflow , Kubeflow, or AWS ... Optimize models for production (e.g., via quantization or pruning) and ensure efficient resource ...
Artificial Intelligence Integration Engineer with Security Clearance
$119.80K - $161.30K/yr
Deploy and manage machine learning models in production using tools like MLflow , Kubeflow, or AWS ... Optimize models for production (e.g., via quantization or pruning) and ensure efficient resource ...
Deploy and manage machine learning models in production using tools like MLflow, Kubeflow, or AWS ... Optimization: Optimize models for production (e.g., via quantization or pruning) and ensure ...
Deploy and manage machine learning models in production using tools like MLflow, Kubeflow, or AWS ... Optimization: Optimize models for production (e.g., via quantization or pruning) and ensure ...
Artificial Intelligence Integration Engineer
$119.70K - $161.10K/yr
Deploy and manage machine learning models in production using tools like MLflow, Kubeflow, or AWS ... Optimize models for production (e.g., via quantization or pruning) and ensure efficient resource ...
Artificial Intelligence Integration Engineer
$119.70K - $161.10K/yr
Deploy and manage machine learning models in production using tools like MLflow, Kubeflow, or AWS ... Optimize models for production (e.g., via quantization or pruning) and ensure efficient resource ...
Deep Learning Quantization information
See Silver Spring, MD salary details
$22.6K is the 25th percentile. Wages below this are outliers.
$11.4K - $23.5K
27% of jobs
$23.5K - $35.6K
0% of jobs
$35.6K - $47.7K
0% of jobs
$47.7K - $59.9K
0% of jobs
$59.9K - $72K
0% of jobs
The median wage is $83.1K / yr.
$72K - $84.1K
25% of jobs
$84.1K - $96.2K
18% of jobs
$104.9K is the 75th percentile. Wages above this are outliers.
$96.2K - $108.4K
7% of jobs
$108.4K - $120.5K
2% of jobs
$120.5K - $132.6K
0% of jobs
$132.6K - $144.7K
21% of jobs
$11.4K
$86.7K
$144.7K
How much do deep learning quantization jobs pay per year?
As of Jun 1, 2026, the average yearly pay for deep learning quantization in Silver Spring, MD is $86,719.00, according to ZipRecruiter salary data. Most workers in this role earn between $74,400.00 and $143,700.00 per year, depending on experience, location, and employer.
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 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 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 is the difference between Deep Learning Quantization vs Machine Learning Engineer?
| Aspect | Deep Learning Quantization | Machine Learning Engineer |
|---|---|---|
| Required Credentials | Advanced degrees in AI, Computer Science, or related fields; knowledge of neural networks | Bachelor's or Master's in CS, Data Science, or related fields; programming skills |
| Work Environment | Research labs, AI development teams, hardware optimization settings | Software development teams, data-driven projects, product-focused environments |
| Industry Usage | AI hardware optimization, model deployment, edge computing | Model 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.
What are popular job titles related to Deep Learning Quantization jobs in Silver Spring, MD? For Deep Learning Quantization jobs in Silver Spring, MD, the most frequently searched job titles are:
What job categories do people searching Deep Learning Quantization jobs in Silver Spring, MD look for? The top searched job categories for Deep Learning Quantization jobs in Silver Spring, MD are:
What cities near Silver Spring, MD are hiring for Deep Learning Quantization jobs? Cities near Silver Spring, MD with the most Deep Learning Quantization job openings:
Full-time
Posted yesterday
Job description
Job Summary
We are seeking a skilled MLOps Engineer to join our team and ensure the seamless deployment, monitoring, and optimization of AI models in production.
The MLOps Engineer will design, implement, and maintain end-to-end machine learning pipelines, focusing on automating model deployment, monitoring model health, detecting data drift, and managing AI-related logging. This role will involve building scalable infrastructure and dashboards for real-time and historical insights, ensuring models are secure, performant, and aligned with business needs.
Key Responsibilities
- Model Deployment: Deploy and manage machine learning models in production using tools like MLflow, Kubeflow, or AWS SageMaker, ensuring scalability and low latency.
- Monitoring and Observability: Build and maintain dashboards using Grafana, Prometheus, or Kibana to track real-time model health (e.g., accuracy, latency) and historical trends.
- Data Drift Detection: Implement drift detection pipelines using tools like Evidently AI or Alibi Detect to identify shifts in data distributions and trigger alerts or retraining.
- Logging and Tracing: Set up centralized logging with ELK Stack or OpenTelemetry to capture AI inference events, errors, and audit trails for debugging and compliance.
- Pipeline Automation: Develop CI/CD pipelines with GitHub Actions or Jenkins to automate model updates, testing, and deployment.
- Security and Compliance: Apply secure-by-design principles to protect data pipelines and models, using encryption, access controls, and compliance with regulations like GDPR or NIST AI RMF.
- Collaboration: Work with data scientists, AI Integration Engineers, and DevOps teams to align model performance with business requirements and infrastructure capabilities.
- Optimization: Optimize models for production (e.g., via quantization or pruning) and ensure efficient resource usage on cloud platforms like AWS, Azure, or Google Cloud.
- Documentation: Maintain clear documentation of pipelines, dashboards, and monitoring processes for cross-team transparency.
Qualifications
- Education: Bachelor’s or Master’s degree in Computer Science, Data Science, Engineering, or a related field.
- Experience:
- 5+ years in MLOps, DevOps, or software engineering with a focus on AI/ML systems.
- Proven experience deploying models in production using MLflow, Kubeflow, or cloud platforms (AWS SageMaker, Azure ML).
- Hands-on experience with observability tools like Prometheus, Grafana, or Datadog for real-time monitoring.
- Technical Skills:
- Proficiency in Python and SQL; familiarity with JavaScript or Go is a plus.
- Expertise in containerization (Docker, Kubernetes) and CI/CD tools (GitHub Actions, Jenkins).
- Knowledge of time-series databases (e.g., InfluxDB, TimescaleDB) and logging frameworks (e.g., ELK Stack, OpenTelemetry).
- Experience with drift detection tools (e.g., Evidently AI, Alibi Detect) and visualization libraries (e.g., Plotly, Seaborn).
- AI-Specific Skills:
- Understanding of model performance metrics (e.g., precision, recall, AUC) and drift detection methods (e.g., KS test, PSI).
- Familiarity with AI vulnerabilities (e.g., data poisoning, adversarial attacks) and mitigation tools like Adversarial Robustness Toolbox (ART).
- Soft Skills:
- Strong problem-solving and debugging skills for resolving pipeline and monitoring issues.
- Excellent collaboration and communication skills to work with cross-functional teams.
- Attention to detail for ensuring accurate and secure dashboard reporting.
- Must be eligible to obtain a Department of Homeland Security EOD clearance ( Requirements 1. US Citizenship, 2. Favorable Background Investigation)
Preferred Qualifications
- Experience with LLM monitoring tools like LangSmith or Helicone for generative AI applications.
- Knowledge of compliance frameworks (e.g., GDPR, HIPAA) for secure data handling.
- Contributions to open-source MLOps projects or familiarity with X platform discussions on #MLOps or #AIOps.
Formed through the strategic union of Sev1Tech and ERT, Entarian is a premier provider of mission-critical engineering and technology solutions. Founded on a legacy of excellence dating back to 1993, Entarian is a product of an evolved and fully diversified engineering and federal technology leader. From deep space to defense and civilian missions, Entarian delivers secure, mission-aligned digital solutions that drive national resilience and operational effectiveness. We don't just support modernization; we define it.
Join the Mission and Start your Career Journey: Apply Directly via our Careers Portal Connect, Referrals & Inquiries? Email the team: careers@entarian.com
Entarian is an Equal Opportunity and Affirmative Action Employer. All qualified applicants will receive consideration for employment without regard to race, color, religion, sex, pregnancy, sexual orientation, gender identity, national origin, age, protected veteran status, or disability status.