ZipRecruiter

Log In

1

Python Ml Developer Jobs in Glendale, CA (NOW HIRING)

CYNET SYSTEMS

ML Engineer

Woodland Hills, CA · On-site

$56 - $61/hr

Expert-level proficiency in Python. * Strong experience in model training, evaluation, and tagging workflows. * Hands-on experience with document extraction and chunking techniques. * Solid ...

LHH US

Director of Engineering, AI/ML

Los Angeles, CA

$250K - $380K/yr

LHH is seeking a Director of Engineering, AI/ML, in a full-time capacity. In this role, you'll lead ... Advanced proficiency in Java, Python, ML frameworks and ML libraries. * Required: Great ...

LHH US

Director of Engineering, AI/ML

Los Angeles, CA

$250K - $380K/yr

LHH is seeking a Director of Engineering, AI/ML, in a full-time capacity. In this role, you'll lead ... Advanced proficiency in Java, Python, ML frameworks and ML libraries. * Required: Great ...

Northern Base

AI/ML Engineer

Woodland Hills, CA · On-site

Hiring Alert | AI/ML Engineer - Generative AI, LLM & ML Engineering Location: Woodland Hills, CA ... Expert-Level Python * Machine Learning, Model Training, Evaluation & Fine-Tuning * Generative AI ...

3ds

AI/ML Developer

Long Beach, CA · On-site

$140K - $160K/yr

... python and experience with machine learning frameworks such as PyTorch, TensorFlow, and Scikit ... prompt engineering, prompt optimization, model evaluation, and AI agent design patterns • ...

Staffingine LLC

AI/ML engineer

Los Angeles, CA · On-site

$123K - $148K/yr

AI/ML engineer Job Location: Los Angeles, CA Job Type: Contract * 6 8 years of Handson experience ... Extensive experience in Python programming languages along with popular frameworks such as Flask ...

Programmers.io

AI/ML Engineer

Woodland Hills, CA · On-site

Position: AI/ML Engineer Location: Woodland Hills, CA/ Mason, OH (Onsite) Key Responsibilities ... Advanced proficiency in Python; exposure to Java/Go is a plus. * Cloud Proficiency: Strong ...

Carter Support Services

AI/ML Engineer

Burbank, CA · On-site

The ideal candidate will bring deep expertise in Python, cloud-based AI deployment (Azure), and ... of DevOps experience , including CI/CD pipelines * 7+ years of MongoDB or similar database ...

Carter Support Services

AI/ML Engineer

Burbank, CA · On-site

$111K - $153K/yr

The ideal candidate will bring deep expertise in Python, cloud-based AI deployment (Azure), and ... of DevOps experience , including CI/CD pipelines * 7+ years of MongoDB or similar database ...

Catalyst Labs

ML Engineer

Manhattan Beach, CA · On-site

... Python (TypeScript experience is a plus). • Hands-on experience with ML frameworks such as ... feature engineering, evaluation strategies, and deployment best practices. Preferred : • ...

YO AI Labs

AI/ML Engineer - Remote

Los Angeles, CA · Remote

$200 - $350/hr

Follow modern secure coding, DevOps, and CI/CD practices. * Document technical decisions and ... Strong Python proficiency for AI/ML development, including REST APIs and SDK integrations. * Hands ...

Staffingine LLC

AI/ML Engineer

Los Angeles, CA · On-site

AI/ML Engineer Job Location: Los Angeles - California - USA Job Type: Contract * 3-5 years of ... Expertise in Python and its data science ecosystem eg Pandas NumPy Scikitlearn * Proven experience ...

next page

Showing results 1-20

All JobsPython Ml Developer JobsPython Ml Developer Jobs in Glendale, CA

Python Ml Developer information

See Glendale, CA salary details

$14

$62

$91

How much do python ml developer jobs pay per hour?

As of Sep 1, 2026, the average hourly pay for python ml developer in Glendale, CA is $62.09, according to ZipRecruiter salary data. Most workers in this role earn between $51.15 and $70.53 per hour, depending on experience, location, and employer.

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 are the key skills and qualifications needed to thrive as a Python ML Developer?

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 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 Glendale, CA?

For Python Ml Developer jobs in Glendale, CA, the most frequently searched job titles are:

What job categories do people searching Python Ml Developer jobs in Glendale, CA look for?

The top searched job categories for Python Ml Developer jobs in Glendale, CA are:

What cities near Glendale, CA are hiring for Python Ml Developer jobs?

Cities near Glendale, CA with the most Python Ml Developer job openings:

Python Ml Developer jobs near you
Infographic showing various Python Ml Developer job openings in Glendale, CA as of August 2026, with employment types broken down into 1% Internship, 89% Full Time, 5% Part Time, and 5% Contract. Highlights an 82% Physical, 5% Hybrid, and 13% Remote job distribution, with an average salary of $129,141 per year, or $62.1 per hour.

Senior Software Engineer - AI/ML

Prosum Inc.

Santa Monica, CA • On-site

$140K - $180K/yr

Other

Posted 19 days ago

Job description

Job Description
Senior Software Engineer - AI/ML
Salary Range: $140,000-$180,000
About the Role
We are seeking a Senior Software Engineer specializing in AI and Machine Learning to join an innovative AI engineering organization. This role sits at the intersection of applied machine learning, LLM product development, and production-grade software engineering.
The Senior Software Engineer will be responsible for building, training, evaluating, and deploying AI/ML models and agentic systems that power intelligent products and enterprise applications. The ideal candidate combines deep hands-on expertise in PyTorch, transformer architectures, LLMs, and the full machine learning lifecycle with the software engineering discipline required to build reliable, scalable AI solutions in production.
This is an opportunity to play a key role in shaping AI capabilities within a large-scale enterprise environment, working closely with engineering, product, data, architecture, and leadership teams.
Key ResponsibilitiesAI / ML Engineering
  • Design, develop, train, fine-tune, and evaluate machine learning models using PyTorch and related libraries.
  • Build and maintain ML training pipelines, experiment tracking workflows, and model evaluation frameworks.
  • Implement transformer-based models and large language model (LLM) integrations for production use cases, including NLP, information extraction, classification, and generation.
  • Apply parameter-efficient fine-tuning techniques such as LoRA, QLoRA, and PEFT to adapt foundation models to domain-specific applications.
  • Design and implement Retrieval-Augmented Generation (RAG) architectures using vector databases and semantic search pipelines.
  • Optimize model inference for latency and throughput through quantization, batching, caching, and other performance techniques.
  • Develop and maintain AI evaluation frameworks, including automated evaluations and tests, to ensure model behavior is reliable, safe, and production-ready.
LLM Integration & Agentic Systems
  • Design and implement LLM-powered agentic workflows using frameworks such as LangChain and LangGraph.
  • Build multi-step reasoning pipelines, tool-calling agents, and autonomous task execution systems that integrate with enterprise data and product APIs.
  • Develop prompt engineering strategies, few-shot templates, structured outputs, and validation mechanisms.
  • Implement runtime guardrails, failure taxonomies, and quality standards for AI-powered applications.
  • Contribute to enterprise AI infrastructure and context-management capabilities that allow AI agents to securely interact with organizational tools, systems, and data.
MLOps & Production Engineering
  • Build and maintain MLOps infrastructure supporting model training, experiment tracking, versioning, and deployment.
  • Containerize and deploy ML services using Docker and Kubernetes and integrate them with CI/CD pipelines.
  • Monitor model performance in production and implement drift detection, feedback loops, and automated retraining processes.
  • Ensure AI systems meet organizational security, privacy, and compliance requirements, including appropriate data minimization and access controls.
  • Partner with data engineering teams to design and maintain feature stores, data pipelines, and training data infrastructure.
Collaboration & Technical Leadership
  • Partner with architecture and AI leadership to define AI architecture patterns, engineering standards, and best practices.
  • Collaborate with product managers, UX designers, and software engineers to translate AI capabilities into well-designed product features.
  • Conduct code reviews with an emphasis on reproducibility, correctness, maintainability, and production readiness.
  • Mentor engineers on AI engineering fundamentals, LLM integration patterns, and responsible AI practices.
  • Stay current with the rapidly evolving AI/ML landscape and evaluate emerging models, frameworks, and techniques.
  • Contribute to organizational AI maturity initiatives and help establish scalable AI engineering practices.
  • Represent AI engineering practices in architecture and technical review forums.
Required Qualifications
  • Bachelor's or Master's degree in Computer Science, Machine Learning, Statistics, Mathematics, or a related quantitative field.
  • 6-10+ years of professional software engineering experience, including at least 3+ years focused on production ML/AI engineering.
  • Expert-level proficiency in Python and strong familiarity with the Python ML/AI ecosystem.
  • Hands-on production experience with PyTorch, including model development, custom training loops, autograd, GPU acceleration/CUDA, and model serialization.
  • Experience with Hugging Face Transformers, Datasets, and PEFT or comparable libraries.
  • Demonstrated experience building and evaluating RAG pipelines, including chunking strategies, embeddings, vector stores, and retrieval evaluation.
  • Production experience integrating LLM APIs and/or open-source LLMs and building reliable prompt engineering systems.
  • Experience with LangChain, LangGraph, or comparable frameworks for agentic and tool-calling workflows.
  • Strong understanding of machine learning fundamentals, including supervised and unsupervised learning, loss functions, regularization, evaluation metrics, and statistical validation.
  • Experience with experiment tracking and reproducible ML workflows using tools such as MLflow, Weights & Biases, or Comet.
  • Working knowledge of Docker and cloud-based ML platforms such as AWS SageMaker, Azure ML, or equivalent.
  • Experience with SQL and NoSQL databases and designing data pipelines for ML training and inference.
Preferred Qualifications
  • Experience with additional deep learning frameworks such as TensorFlow or JAX, or experience with framework interoperability such as ONNX.
  • Familiarity with computer vision or speech/audio processing.
  • Experience with model compression techniques including quantization, pruning, and knowledge distillation.
  • Experience serving ML models at scale using Triton Inference Server, TorchServe, Ray Serve, or similar technologies.
  • Contributions to open-source ML projects or published research, technical papers, patents, or technical blog posts.
  • Experience with responsible AI frameworks, bias evaluation, model governance, and AI risk management.
  • Familiarity with Model Context Protocol (MCP) and development of MCP servers or comparable tool-integration architectures.
  • Experience working in payroll, fintech, media, enterprise SaaS, or other complex enterprise environments.
  • Experience with Kubernetes-based ML workload orchestration, such as Kubeflow or similar platforms.
Work Environment & Additional Requirements
  • Hybrid work environment with flexibility for remote work.
  • Ability to participate in an on-call rotation as needed for production AI incidents and model deployment events.
  • Access to cloud-based, GPU-accelerated computing environments for model training and experimentation.
  • Ability to work for extended periods at a computer workstation.
  • Ability to use standard computer equipment, including keyboard and mouse.
  • Occasional availability for early-morning or evening meetings to collaborate with distributed teams or international partners.
What You'll Bring
The successful candidate will bring a combination of strong software engineering fundamentals, deep AI/ML expertise, and a production mindset. You should be comfortable moving from experimentation and model development through deployment, monitoring, and continuous improvement.
You will have the opportunity to work on challenging AI problems, influence technical direction, mentor other engineers, and help build intelligent systems that deliver measurable value within an enterprise environment.
Please view our Privacy Policy.

Apply