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Pytorch Developer Jobs in Berkeley, CA (NOW HIRING)

RIT Solutions

Machine Learning Engineer

Fremont, CA

... PyTorch. The ideal candidate will collaborate closely with cross-functional teams across Production, Process Engineering, Controls, and Quality to address critical operational challenges. This ...

Sciforium

GPU Kernel Engineer

San Francisco, CA · On-site

$190K - $250K/yr

Integrate low-level GPU kernels into frameworks such as PyTorch, JAX, and custom internal runtimes ... Collaborate with ML researchers, distributed systems engineers, and model-serving teams to optimize ...

Tror AI for everyone

MLOPS Engineer

Concord, CA · On-site

... g., scikit-learn, XGBoost, TensorFlow, PyTorch). * Experience with cloud platforms and ... DevOps practices. * Good communication skills and able to manage stakeholders. Thanks & Regards ...

Skild AI

Research Engineer

San Mateo, CA · On-site

Position Overview We are hiring Research Engineers to develop scalable robotic systems aimed at ... Proficiency in Python and at least one deep learning library such as PyTorch, TensorFlow, JAX, etc.

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Pytorch Developer information

What is a PyTorch Developer?

A PyTorch Developer is a software engineer or data scientist who specializes in using PyTorch, an open-source machine learning library, to build and deploy deep learning models. Their responsibilities typically include designing neural network architectures, training and evaluating models, and optimizing code for performance. PyTorch Developers work in fields such as artificial intelligence, computer vision, and natural language processing, collaborating with teams to solve complex problems using machine learning. They are proficient in Python and have a strong understanding of deep learning concepts. Additionally, they often contribute to research, development, and the deployment of AI solutions in production environments.

What are the key skills and qualifications needed to thrive as a Pytorch Developer, and why are they important?

To thrive as a Pytorch Developer, you need strong programming skills in Python, a solid grasp of machine learning concepts, and experience with deep learning frameworks—especially PyTorch itself. Familiarity with tools like CUDA, Jupyter Notebooks, and version control systems (e.g., Git) is typically expected, along with knowledge of cloud platforms or relevant certifications. Problem-solving ability, effective collaboration, and clear communication are crucial soft skills for success in this role. These skills and qualities are vital for efficiently building, optimizing, and deploying machine learning models in real-world applications.

What is the difference between Pytorch Developer vs Machine Learning Engineer?

AspectPytorch DeveloperMachine Learning Engineer
Required CredentialsBachelor's or higher in CS, experience with PyTorchBachelor's or higher in CS, data science, or related field, with ML experience
Work EnvironmentResearch labs, AI startups, tech companies focusing on deep learningTech companies, finance, healthcare, often involving deployment and scaling ML models
Industry UsagePrimarily in AI research and development teamsAcross industries implementing ML solutions in production

While both roles require knowledge of machine learning and experience with PyTorch, a Pytorch Developer mainly focuses on developing and optimizing deep learning models using PyTorch. A Machine Learning Engineer often has a broader scope, including deploying, maintaining, and scaling ML models across various platforms and industries.

What are some common challenges Pytorch Developers face when deploying machine learning models to production environments?

Pytorch Developers often encounter challenges when transitioning models from research to production, such as optimizing model performance for inference speed and memory usage, ensuring compatibility with deployment frameworks like TorchScript or ONNX, and managing dependencies across different systems. Additionally, integrating PyTorch models into existing software stacks and maintaining reproducibility can be complex. Collaborating closely with DevOps and data engineering teams is crucial to address these issues and ensure smooth deployment.
What are popular job titles related to Pytorch Developer jobs in Berkeley, CA? For Pytorch Developer jobs in Berkeley, CA, the most frequently searched job titles are:
What job categories do people searching Pytorch Developer jobs in Berkeley, CA look for? The top searched job categories for Pytorch Developer jobs in Berkeley, CA are:
What cities near Berkeley, CA are hiring for Pytorch Developer jobs? Cities near Berkeley, CA with the most Pytorch Developer job openings:
Pytorch Developer jobs near you
Machine Learning Engineer

Machine Learning Engineer

RIT Solutions

Fremont, CA

Other

Posted 4 days ago

Job description

Machine Learning Engineer

Location: Fremont, CA Duration: 12+ Months Tesla/ $65

About the Role

Our direct client is seeking a highly skilled Machine Learning Engineer to join their Software Machine Learning and Computer Vision team. In this role, you will be responsible for designing, developing, and deploying advanced machine learning solutions that support factory and warehouse operations. You will transform complex and ambiguous business challenges into scalable, end-to-end solutions using a variety of machine learning techniques, including supervised learning, computer vision, and deep learning frameworks such as PyTorch. The ideal candidate will collaborate closely with cross-functional teams across Production, Process Engineering, Controls, and Quality to address critical operational challenges. This position requires hands-on experience with model development, deployment, monitoring, and maintenance in production environments, as well as the ability to work with diverse and multi-modal datasets, including images, sensor data, voice, text, and structured data.

Key Responsibilities
  • Design, develop, and deploy machine learning models to enhance factory and warehouse operations.
  • Partner with cross-functional stakeholders to identify and solve high-impact operational challenges.
  • Build and maintain end-to-end machine learning pipelines, including data ingestion, preprocessing, model training, deployment, and monitoring.
  • Evaluate and benchmark machine learning models using statistical methodologies to ensure optimal performance and business value.
  • Develop robust monitoring and alerting mechanisms to ensure reliability and rapid issue resolution for production models.
  • Integrate and analyze heterogeneous datasets, including image data, sensor outputs, voice recordings, text, and tabular datasets.
  • Write clean, scalable, and maintainable code to translate research concepts into production-ready solutions.
Required Qualifications
  • Strong proficiency in Python, particularly for high-performance and data-intensive applications.
  • Hands-on experience with at least one modern deep learning framework, such as PyTorch, JAX, or TensorFlow.
  • Expertise in one or more of the following domains:
    • Computer Vision
    • Large Language Models (LLMs)
    • Recommender Systems
    • Operations Research
  • Solid understanding of statistics and experimental methods for model evaluation and comparison.
  • Proven experience deploying, monitoring, and maintaining machine learning solutions in production environments.
  • Strong commitment to writing clean, modular, and sustainable code.
Preferred Qualifications
  • Experience working within manufacturing, industrial automation, logistics, or warehouse environments.
  • Familiarity with multi-modal data processing and integration techniques.
  • Strong analytical and problem-solving abilities with the capacity to thrive in fast-paced and ambiguous environments.
  • Excellent communication and collaboration skills, with the ability to work effectively across cross-functional teams.

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