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Tensorflow Pytorch Jobs in Washington (NOW HIRING)

S. degree in a technical field and 3+ years of experience • Python and ML frameworks (TensorFlow, PyTorch, Scikit-learn) • Experience with data processing and feature engineering • ...

Senior Data Scientist

Washington, DC · On-site

$130 - $150/hr

Expert proficiency in advanced ML and AI: deep learning (TensorFlow, PyTorch, Keras), ensemble methods (XGBoost, Random Forest), Bayesian methods, and multi-objective optimization algorithms.

Strong proficiency in Python (NumPy, Pandas, scikit-learn, TensorFlow, PyTorch, Transformers). * Full Stack: Experience with backend frameworks (Django, Flask, FastAPI) and frontend frameworks (React ...

Docker, Kubernetes, MLflow, TensorFlow, PyTorch, Tableau, Power BI EDUCATION • Bachelor of Science in Statistics and Mathematics or other related field o preferred Master of Science in Computer ...

New

Experience with machine learning frameworks (e.g., Scikit-learn, TensorFlow, PyTorch) * Strong foundation in statistics, probability, and data analysis techniques * Experience with SQL and working ...

Experience with AI/ML tools and frameworks (e.g., TensorFlow, PyTorch). * Strong analytical and problem-solving abilities. * Excellent communication skills and teamwork capabilities. * Experience ...

Showing results 21-40

Tensorflow Pytorch information

What are TensorFlow and PyTorch?

TensorFlow and PyTorch are two of the most popular open-source deep learning frameworks used by researchers and developers to build, train, and deploy machine learning models. TensorFlow, developed by Google, offers robust support for production environments and has a large ecosystem. PyTorch, developed by Facebook, is known for its flexibility, ease of use, and dynamic computational graph, making it popular in academia and research. Both frameworks support a wide range of neural network architectures and are used extensively for tasks such as computer vision, natural language processing, and reinforcement learning.

What are the key skills and qualifications needed to thrive as a deep learning engineer specializing in TensorFlow and PyTorch?

To thrive as a Deep Learning Engineer with a focus on TensorFlow and PyTorch, you need a strong background in computer science, mathematics, and machine learning, typically supported by a relevant degree. Proficiency in programming languages like Python, experience with TensorFlow and PyTorch frameworks, and familiarity with cloud platforms or GPU computing are essential. Analytical thinking, problem-solving, and effective communication are standout soft skills for collaborating with teams and interpreting model results. These skills are crucial for developing, deploying, and optimizing AI models that drive innovation and solve complex real-world problems.

How do TensorFlow/PyTorch engineers typically collaborate with data scientists and other team members in a production environment?

TensorFlow and PyTorch engineers often work closely with data scientists to transform experimental machine learning models into efficient, scalable production solutions. Collaboration involves frequent code reviews, shared development environments, and regular meetings to align model requirements with deployment constraints. Engineers also coordinate with DevOps teams to ensure smooth integration and monitoring of models in production. Strong communication skills and a willingness to iterate on solutions are essential for bridging the gap between research and real-world application.

What is the difference between Tensorflow Pytorch vs Data Scientist?

AspectTensorflow PytorchData Scientist
Required SkillsDeep learning frameworks, Python, machine learningData analysis, statistical skills, Python/R, machine learning
Work EnvironmentAI/ML development, research, software engineeringData analysis, reporting, business insights
Industry UsageAI/ML projects, research labs, tech companiesBusiness, finance, healthcare, tech

Tensorflow and Pytorch are deep learning frameworks used primarily by AI/ML developers, while Data Scientists utilize these tools for data analysis and modeling. Although their skill sets overlap, Tensorflow Pytorch focus on model development, whereas Data Scientists apply these models to derive insights and inform decisions.

What job categories do people searching Tensorflow Pytorch jobs in Washington look for?

The top searched job categories for Tensorflow Pytorch jobs in Washington are:

What cities in Washington are hiring for Tensorflow Pytorch jobs?

Cities in Washington with the most Tensorflow Pytorch job openings:

CLOUD - AI/ML Engineer

Steel Point Solutions

Fort George G Meade, MD • On-site

$61.75 - $82.50/hr

Full-time

Posted 14 days ago


Job description

Steel Point Solutions is an amazing SBA Certified (8a), HUBZone, Small Disadvantaged Business (SDB) and a Woman Owned Small Business (WOSB) company. Established in 2013 with a vision of offering world class, integrated business solutions for all levels of Government and commercial enterprises. We are represented by a team of talented and qualified professionals who know how essential efficient, cost-effective integrated solutions are to your organization's success. Leveraging these resources, we strive daily to lead the industry in program management and service delivery. 

Role Summary

The AI/ML Engineer is responsible for designing, developing, and deploying machine learning models and artificial intelligence solutions that drive Steel Point's data-driven initiatives. This role involves creating algorithms, working with large datasets, and implementing AI/ML solutions to solve complex business problems. The AI/ML Engineer will collaborate with data scientists, software engineers, and other stakeholders to integrate and optimize AI/ML systems within existing infrastructure.

Key Roles & Responsibilities

  • Model Development: Design, develop, and train machine learning models, including supervised, unsupervised, and reinforcement learning algorithms.
  • Data Processing: Prepare and preprocess large datasets for training and validation of AI/ML models, including data cleaning, feature engineering, and transformation.
  • Algorithm Implementation: Implement and optimize AI/ML algorithms using industry-standard libraries and frameworks (e.g., TensorFlow, PyTorch, Scikit-Learn).
  • Deployment: Deploy AI/ML models into production environments, ensuring scalability, reliability, and performance.
  • Performance Monitoring: Monitor and evaluate model performance, making necessary adjustments and improvements to enhance accuracy and efficiency.
  • Collaboration: Work closely with data scientists, software engineers, and business stakeholders to understand requirements and integrate AI/ML solutions into applications and systems.
  • Documentation: Create and maintain documentation for AI/ML models, including design, development, and deployment processes.
  • Innovation: Stay updated with the latest advancements in AI/ML technologies and methodologies, applying new techniques to improve existing models and solutions.
  • Ethics and Compliance: Ensure AI/ML solutions adhere to ethical guidelines and regulatory requirements, including fairness, transparency, and privacy.

Required Qualifications

  • Bachelor's degree in Computer Science, Data Science, Engineering, Mathematics, or a related field.
  • 3+ years of experience in AI/ML engineering, including hands-on experience with model development and deployment.
  • 3+ years of experience with data processing and manipulation using tools such as Pandas, NumPy, and SQL.
  • Proven experience with machine learning frameworks and libraries (e.g., TensorFlow, PyTorch, Keras)
  • Candidate Must Have an Active Top Secret SCI Poly Security Clearance. 

Preferred Qualifications

  • A Master's degree or PhD in a relevant field is preferred
  • Certifications:
    • AI/ML-related certifications (e.g., Google Cloud Certified - Professional Machine Learning Engineer, AWS Certified Machine Learning - Specialty) are preferred.
    • Certifications in data science or analytics (e.g., Certified Data Scientist) are a plus.

Skills and Competencies

  • Machine Learning: Strong knowledge of machine learning algorithms, techniques, and best practices.
  • Data Engineering: Proficiency in data processing, feature engineering, and working with large datasets.
  • Technical Skills: Experience with AI/ML frameworks and libraries, and programming languages such as Python, R, or Java.
  • Problem-Solving: Strong analytical and problem-solving skills to address complex AI/ML challenges and optimize model performance.
  • Collaboration: Ability to work effectively with cross-functional teams, including data scientists, engineers, and business stakeholders.
  • Communication: Excellent verbal and written communication skills, with the ability to convey complex technical concepts to non-technical audiences.
  • Ethical Awareness: Understanding of ethical considerations and regulations related to AI/ML technologies, including fairness and privacy.

Candidates from Historically Underutilized Business Zones (HUBZone) are strongly encouraged to apply. To determine whether you reside in a HUBZone, visit: https://maps.certify.sba.gov/hubzone/map.