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Python Ml Developer Jobs in San Ramon, CA (NOW HIRING)

Career Renew

Applied ML Engineer

San Francisco, CA · Hybrid

$190K - $250K/yr

Career Renew is recruiting for one of its clients an Applied ML Engineer - this is a hybrid role in ... Proficiency in Python and experience with deep learning frameworks such as PyTorch or TensorFlow.

Typeface

Principal ML Engineer

Palo Alto, CA · Hybrid

$230K - $260K/yr

Proficiency in Python and systems-level programming (C/C++ or equivalent) * Bonus: Experience building ML platforms used across multiple products or teams * Bonus: Prior role as a Staff+ / Principal ...

Quest Global

Data AI/ML Engineer

Sunnyvale, CA

$100K - $105K/yr

Job Requirements Quest Global delivers world-class end-to-end engineering solutions by leveraging ... Python, TensorFlow, PyTorch, scikit-learn * ML pipelines and data processing systems. * ML models ...

Gap, Inc.

Sr. ML Engineer - ML & Applied AI

San Francisco, CA · On-site

$123K - $168.90K/yr

Strong programming expertise in Python and solid software engineering fundamentals (data structures, system design, APIs) * Extensive experience with ML frameworks such as scikit-learn, XGBoost ...

Quest Global

Data AI/ML Engineer

Sunnyvale, CA · On-site

$100K - $105K/yr

Job Requirements Quest Global delivers world-class end-to-end engineering solutions by leveraging ... Python, TensorFlow, PyTorch, scikit-learn * ML pipelines and data processing systems. * ML models ...

Quest Global

Data AI/ML Engineer

Sunnyvale, CA · On-site

$100K - $105K/yr

Job Requirements Quest Global delivers world-class end-to-end engineering solutions by leveraging ... Python, TensorFlow, PyTorch, scikit-learn * ML pipelines and data processing systems. * ML models ...

Sonar

Forward Deployed Engineer, ML

San Francisco, CA · On-site

Understanding of modern ML frameworks, programming languages including Python, and deployment technologies (Docker, Kubernetes, cloud services like SageMaker/Vertex AI/Azure AI). * Value-Driven ...

Cooley LLP

Senior AI/ML Engineer

Palo Alto, CA

$122.80K - $168.70K/yr

Senior AI/ML Engineer Cooley is seeking a Senior AI/ML Engineer to join the Practice Engineering ... Strong proficiency in Python for building and operating LLM-powered applications and agentic ...

Cooley LLP

Senior AI/ML Engineer

San Francisco, CA

$123.10K - $169.10K/yr

Senior AI/ML Engineer Cooley is seeking a Senior AI/ML Engineer to join the Practice Engineering ... Strong proficiency in Python for building and operating LLM-powered applications and agentic ...

Cooley LLP

Senior AI/ML Engineer

San Francisco, CA

$123.10K - $169.10K/yr

Senior AI/ML Engineer Cooley is seeking a Senior AI/ML Engineer to join the Practice Engineering ... Strong proficiency in Python for building and operating LLM-powered applications and agentic ...

Cooley

Senior AI/ML Engineer

Palo Alto, CA

$122.80K - $168.70K/yr

Senior AI/ML Engineer Cooley is seeking a Senior AI/ML Engineer to join the Practice Engineering ... Strong proficiency in Python for building and operating LLM-powered applications and agentic ...

Cooley

Senior AI/ML Engineer

San Francisco, CA

$123.10K - $169.10K/yr

Senior AI/ML Engineer Cooley is seeking a Senior AI/ML Engineer to join the Practice Engineering ... Strong proficiency in Python for building and operating LLM-powered applications and agentic ...

Wispr Flow

ML Engineer

San Francisco, CA · On-site

$130K - $240K/yr

About the Role As a ML engineer at Wispr, you'll play a crucial role in building the first capable ... Fluency in Python and LLM development * Attention to detail and eagerness to learn * Aptitude and ...

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How much do python ml developer jobs pay per hour?

As of May 31, 2026, the average hourly pay for python ml developer in San Ramon, CA is $65.51, according to ZipRecruiter salary data. Most workers in this role earn between $53.99 and $74.42 per hour, depending on experience, location, and employer.

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

To thrive as a Python ML Developer, you need strong programming skills in Python, a solid understanding of machine learning algorithms, and a background in mathematics or statistics, often supported by a degree in computer science, engineering, or a related field. Familiarity with tools and libraries such as TensorFlow, scikit-learn, PyTorch, and version control systems like Git is essential, along with experience using data visualization and cloud platforms. Critical soft skills include problem-solving, adaptability, and effective communication to collaborate with cross-functional teams and explain complex models to stakeholders. These skills ensure the successful development, deployment, and maintenance of machine learning solutions that drive business value.

What are some common challenges Python ML Developers face when deploying machine learning models to production?

Python ML Developers often encounter challenges such as ensuring model scalability, managing dependencies, and maintaining reproducibility when deploying models into production environments. Integrating machine learning models with existing systems can require close collaboration with DevOps and software engineering teams to streamline workflows and automate deployment pipelines. Additionally, monitoring model performance over time and handling data drift are crucial responsibilities to ensure continued accuracy and reliability of deployed solutions.

What does a Python ML Developer do?

A Python ML Developer designs, builds, and deploys machine learning models using the Python programming language. They work with large datasets, clean and process data, select appropriate algorithms, and use libraries like TensorFlow, PyTorch, or scikit-learn to implement solutions. Their work often involves collaborating with data scientists and engineers to integrate machine learning models into applications. Additionally, they may be responsible for testing, tuning, and optimizing models to achieve the best possible performance in real-world scenarios.

What is the difference between Python Ml Developer vs Data Scientist?

AspectPython Ml DeveloperData Scientist
Required CredentialsBachelor's in CS, Data Science, or related; Python, ML certificationsBachelor's/Master's in Data Science, Statistics, or related; Python, ML certifications
Work EnvironmentSoftware development teams, AI/ML projectsResearch, data analysis, modeling teams
Employer & Industry UsageTech companies, startups, AI firmsFinance, healthcare, tech, research institutions
Common Search & ComparisonYesYes

Python ML Developers focus on building and deploying machine learning models using Python, often working closely with software engineering teams. Data Scientists analyze data, create models, and generate insights, often using Python along with statistical tools. While both roles require Python and ML knowledge, Python ML Developers are more involved in implementation and deployment, whereas Data Scientists focus on data analysis and research.

What are popular job titles related to Python Ml Developer jobs in San Ramon, CA? For Python Ml Developer jobs in San Ramon, CA, the most frequently searched job titles are:
What job categories do people searching Python Ml Developer jobs in San Ramon, CA look for? The top searched job categories for Python Ml Developer jobs in San Ramon, CA are:
What cities near San Ramon, CA are hiring for Python Ml Developer jobs? Cities near San Ramon, CA with the most Python Ml Developer job openings:
Python Ml Developer jobs near you
ML Infrastructure Engineer

ML Infrastructure Engineer

Mach9 Robotics Inc

San Francisco, CA • On-site

$160K - $200K/yr

Full-time

Posted 6 days ago

Job description

The role
At Mach9, ML infrastructure engineers build and maintain the systems that power production AI models for civil engineering and surveying. Our ML pipeline spans 10,000+ miles of labeled survey data, image segmentation networks, and 3D prediction models serving real-time inference to surveyors and engineers in the field.
This role is ideal for mid-career ML infrastructure engineers with experience building for both training and inference.
You'll build training pipelines that handle deep transformer models on hundreds of terabytes of 3D point cloud and image data. You'll also architect our inference infrastructure, delivering both heavy offline detection algorithms and real-time responsive inference that integrates directly with our CAD software.
Responsibilities
  • Design and build a centralized system for versioning training data, generated datasets, and model artifacts, with full lineage tracking from raw source data through to trained model outputs.
  • Develop and maintain reliable, reproducible ML training and data generation pipelines.
  • Refactor and harden existing training and data generation scripts into composable, testable, and maintainable components.
  • Create CI/CD workflows for validating data pipelines and model training runs, including automated correctness checks and regression detection.
  • Build tooling that enables ML engineers to launch, monitor, and debug training jobs with minimal friction.
  • Optimize and scale real-time model inference services to meet latency and throughput requirements in production, including profiling, batching strategies, and resource-efficient serving.
  • Own the deployment path from trained model artifact to production endpoint, ensuring reliable rollouts, rollback, and monitoring.

Requirements
  • 3+ years of work experience in relevant fields.
  • Bachelor's or Master's degree in Computer Science, Engineering, or equivalent experience.
  • Strong communication skills and the ability to work closely with ML researchers and engineers to understand their workflows and translate them into robust systems.
  • Experience designing and building data versioning, artifact management, or dataset lineage systems (e.g., DVC, LakeFS, Weights & Biases, or custom solutions).
  • Hands-on experience with ML pipeline orchestration tools (e.g., Airflow, Prefect, Metaflow, or similar).
  • Experience with model serving and inference optimization - profiling latency, reducing memory footprint, or scaling serving infrastructure to meet real-time constraints.
  • Ability to read and refactor ML training code - you don't need to design model architectures, but you need to understand what training pipelines are doing well enough to make them reliable.
  • Proficient with Python, PyTorch.

Bonus qualifications
  • Familiarity with AWS infrastructure services.
  • Experience with containerized ML workflows and GPU-accelerated training environments.
  • Experience with model optimization techniques (e.g., quantization, TensorRT, ONNX Runtime, distillation).
  • Knowledge of infrastructure-as-code tools (e.g., AWS CDK, Terraform).
  • Experience building or operating ML systems that handle large unstructured datasets (imagery, 3D data, sensor data).

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