2

Day Shift No Experience Machine Learning Jobs in California

Real-world experience deploying and maintaining machine learning solutions in production environments. * Passion for clean, sustainable, and modular code to bring research concepts to practical ...

We value hard workers who have no qualms working with terabyte-scale datasets, who are interested ... Our ideal candidate has experience creating a working machine learning-powered project from the ...

You may have the flexibility to work from home one or more days per week. This position will be ... Experience with high-performance computing, multi-node, multi-GPU, distributed training.

We value hard workers who have no qualms working with terabyte-scale datasets, who are interested ... Our ideal candidate has experience creating a working machine learning-powered project from the ...

... single day! As a fundamental tool for human activity, Maps technology is evolving and new ... Java, C++, Python Knowledge and experience with one of Tensorflow/Pytorch/Jax frameworks. Excellent ...

Our tight knit hardware and software team is comprised of experts in product and UX development ... within 30 days of employment. We are an open PTO company. Occasional travel may be required ...

Our tight knit hardware and software team is comprised of experts in product and UX development ... within 30 days of employment. We are an open PTO company. Occasional travel may be required ...

Showing results 21-40

Day Shift No Experience Machine Learning information

What is the difference between Day Shift No Experience Machine Learning vs Data Analyst?

AspectDay Shift No Experience Machine LearningData Analyst
Required CredentialsBasic understanding of algorithms, no formal degree neededBachelor's in related field often preferred
Work EnvironmentTech companies, startups, or research labsCorporate offices, consulting firms, or finance
Employer & Industry UsageTech, AI, and software industriesBusiness, marketing, finance, and healthcare
Common Search & Comparison IntentEntry-level, no experience, beginner rolesData analysis, reporting, entry-level data jobs

While both roles may require basic technical skills, Day Shift No Experience Machine Learning positions focus on understanding algorithms and models with minimal experience, often in tech environments. Data Analysts typically analyze data sets to generate reports and insights, often requiring a degree. The roles differ in industry focus and skill sets but share an entry-level nature for those starting in data-related fields.

How to get into day shift no experience machine learning?

To get into a day shift machine learning role with no experience, focus on building foundational skills in programming languages like Python, understanding basic machine learning concepts, and gaining familiarity with tools such as scikit-learn or TensorFlow. Entry-level positions often require a relevant degree or certification, and demonstrating a willingness to learn through online courses or projects can improve your chances.

What cities in California are hiring for Day Shift No Experience Machine Learning jobs?

Cities in California with the most Day Shift No Experience Machine Learning job openings:

Infographic showing various Day Shift No Experience Machine Learning job openings in California as of August 2026, with employment types broken down into 1% As Needed, 75% Full Time, 19% Part Time, and 5% Contract. Highlights an 92% Physical, 2% Hybrid, and 6% Remote job distribution.

Machine Learning Engineer

MM International

Fremont, CA • On-site

Contractor

Re-posted 2 days ago


Job description

Role: Machine Learning Engineer

Location: Fremont, CA 

 

once the documents are verified, a Codility assessment will be shared with the candidate, where they need to score a minimum of 70% and post that, a general video screening with PV. Then we send the submission to the client

About the Role:

Our direct client is hiring a Machine Learning Engineer for their software machine learning and computer vision team to design, develop, and implement critical machine learning models supporting factory and warehouse operations. You will transform ambiguous problem statements into robust end-to-end solutions using a variety of machine learning techniques and tools, including supervised learning, convolutional neural networks, and modern frameworks such as PyTorch and Pandas.

You will collaborate closely with partners in production, process, controls, and quality to deliver solutions for the most challenging problems in our operations. Your work will involve evaluating and deploying models in production environments, ensuring rapid and reliable alerting systems, and addressing operational issues as they arise. You must be adept at handling diverse, heterogeneous datasets that span multiple modalities, including images, multi-spectral sensor outputs, voice, text, and tabular data.

Responsibilities

  • Design, develop, and deploy machine learning models for factory and warehouse environments.
  • Collaborate with cross-functional teams to identify, define, and solve high-impact operational challenges.
  • Build and maintain end-to-end machine learning pipelines, from data collection and preprocessing to model deployment and monitoring.
  • Evaluate and compare models using statistical methods to ensure optimal performance and feasibility.
  • Ensure robust alerting and monitoring systems are in place for deployed models to address issues rapidly.
  • Work with diverse datasets, integrating multiple data types such as images, sensor data, voice, text, and tabular information.
  • Write clean, modular, and sustainable code to translate research ideas into production-ready solutions.

Minimum Requirements

  • In-depth knowledge of Python for high-performance, data-intensive applications.
  • Proficiency with at least one modern deep learning framework (e.g., PyTorch, Jax, TensorFlow).
  • Expertise in one or more of the following areas: computer vision, large language models, recommender systems, or operations research.
  • Foundational knowledge of statistics for model comparison and performance assessment.
  • Real-world experience deploying and maintaining machine learning solutions in production environments.
  • Passion for clean, sustainable, and modular code to bring research concepts to practical implementation.

Preferred Qualifications

  • CI/CD, Kubernetes, MLflow, TensorFlow, PyTorch, AWS.
  • Experience working in manufacturing, industrial automation, or warehouse environments.
  • Familiarity with multi-modal data integration and analysis.
  • Strong problem-solving skills and the ability to thrive in ambiguous, fast-paced settings.
  • Excellent communication skills for cross-functional teamwork.