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Tensorflow Pytorch Jobs in Oregon (NOW HIRING)

The Avian Consulting LLC

MLOps Engineer

Portland, OR · On-site

Strong experience with Python and ML frameworks such as TensorFlow, PyTorch, or Scikit-learn. * Hands-on experience with MLOps tools such as MLflow, Kubeflow, SageMaker, Vertex AI, or Azure ML.

New

SOSi

Senior Data Scientist

Myrtle Point, OR

Personnel must have demonstrated experience in building and validating AI/ML models using Python, TensorFlow, PyTorch, or Scikit-learn, integrating models into production environments, and optimizing ...

SOSi

Senior Data Scientist

OR · On-site +1

Personnel must have demonstrated experience in building and validating AI/ML models using Python, TensorFlow, PyTorch, or Scikit-learn, integrating models into production environments, and optimizing ...

SOSi

Senior Data Scientist

OR

Personnel must have demonstrated experience in building and validating AI/ML models using Python, TensorFlow, PyTorch, or Scikit-learn, integrating models into production environments, and optimizing ...

Nike

Senior Software Engineer

Beaverton, OR · On-site

$127K - $168K/yr

... TensorFlow, PyTorch) • Lakehouse architecture • Large-Scale Data Preprocessing • Programming (Python, SQL, C++) • LLM Models (Generative AI, RAG) • Data Governance Apply at www.Nike.com ...

Nike

Senior Software Engineer

Beaverton, OR · On-site

$127K - $168K/yr

... TensorFlow, PyTorch, Scikit-learn) • AWS Serverless Architecture (Lambda, API Gateway, IAM) • Infrastructure as Code at Scale (Terraform) • Kubernetes based Production Orchestration (EKS) • ...

LMI

Hands-on experience developing machine learning models using tools such as Scikit-Learn, MLlib, TensorFlow, PyTorch, etc. * Practical experience in deploying AI/ML models in production web-based ...

Intel

AI Performance Library Architect

Hillsboro, OR · On-site

$170K - $315K/yr

... Tensorflow, Pytorch, ONNX Runtime, and more. In this role, you will be responsible for design, development, and maintenance of new functionality in oneDNN to enable performance critical portions of ...

SentinelOne

Senior Backend Software Engineer, ObservoAI

OR · Remote

$122K - $161K/yr

Deep expertise in database technologies (SQL and NoSQL) and advanced experience with machine learning frameworks (TensorFlow, PyTorch) and MLOps practices for production ML systems. * Expert ...

Instacart

OR · On-site

Strong coding (Python) and data fluency (SQL/Pandas), with expertise in classic ML techniques (e.g., XGBoost) and deep learning frameworks (TensorFlow/PyTorch). * Excellent analytical skills and ...

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Tensorflow Pytorch information

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.

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 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.

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 are popular job titles related to Tensorflow Pytorch jobs in Oregon? For Tensorflow Pytorch jobs in Oregon, the most frequently searched job titles are:
What job categories do people searching Tensorflow Pytorch jobs in Oregon look for? The top searched job categories for Tensorflow Pytorch jobs in Oregon are:
Tensorflow Pytorch jobs near you

MLOps Engineer

The Avian Consulting LLC

Portland, OR • On-site

Other

This job post has expired today. Applications are no longer accepted.

Job description

Job Title: MLOps Engineer
Location: Portland, OR (5 days Onsite), they may ask for F2F client interview.

Job Description

We are seeking an experienced MLOps Engineer to join our team onsite in Portland, OR. The ideal candidate will be responsible for designing, deploying, automating, and maintaining machine learning pipelines and infrastructure. You will work closely with data scientists, software engineers, and cloud teams to operationalize ML models and ensure scalable, secure, and reliable AI/ML solutions.

Key Responsibilities

  • Design, build, and maintain end-to-end MLOps pipelines for model training, testing, deployment, and monitoring.
  • Automate ML workflows using CI/CD best practices.
  • Deploy and manage machine learning models in production environments.
  • Develop scalable data and model pipelines on cloud platforms.
  • Monitor model performance, data drift, and system health.
  • Collaborate with data scientists to productionize ML models.
  • Implement model versioning, experiment tracking, and artifact management.
  • Optimize infrastructure for performance, scalability, and cost efficiency.
  • Ensure security, governance, and compliance for ML platforms.
  • Troubleshoot production issues and improve operational reliability.

Required Skills

  • 5+ years of experience in DevOps, Data Engineering, or MLOps.
  • Strong experience with Python and ML frameworks such as TensorFlow, PyTorch, or Scikit-learn.
  • Hands-on experience with MLOps tools such as MLflow, Kubeflow, SageMaker, Vertex AI, or Azure ML.
  • Experience with containerization technologies like Docker and Kubernetes.
  • Strong knowledge of CI/CD tools such as Jenkins, GitHub Actions, GitLab CI, or Azure DevOps.
  • Experience with cloud platforms (AWS, Azure, or Google Cloud Platform).
  • Experience with Infrastructure as Code tools such as Terraform or CloudFormation.
  • Knowledge of model monitoring, logging, and observability tools.
  • Strong understanding of Git version control and software development best practices.
  • Experience with Linux environments and shell scripting.

 

 

Preferred Qualifications

  • Experience with Generative AI, LLM deployment, or RAG-based applications.
  • Familiarity with Apache Airflow, Kafka, or Spark.
  • Knowledge of feature stores and model registries.

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