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Entry Level Machine Learning Jobs in Campbell, CA

Nuro

Associate Fleet Technician, Bay Area

Santa Clara, CA · On-site

$28.25 - $38/hr

The Associate Fleet Technician is a critical, entry-level technical role that provides the ... machinery. * Technical Troubleshooting Mindset: Enjoy solving problems that require learning and ...

Nuro

Associate Fleet Technician, Bay Area

Santa Clara, CA · On-site

$28.25 - $38/hr

The Associate Fleet Technician is a critical, entry-level technical role that provides the ... machinery. * Technical Troubleshooting Mindset: Enjoy solving problems that require learning and ...

Aviso, LLC

Junior Quality Engineer

Fremont, CA · On-site

$75K - $90K/yr

This is an exceptional entry-level career pathway designed for an early-career professional looking ... In this role, you will take a deeply hands-on approach from day one, learning our Quality ...

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Entry Level Machine Learning information

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How much do entry level machine learning jobs pay per hour?

As of Aug 8, 2026, the average hourly pay for entry level machine learning in Campbell, CA is $20.21, according to ZipRecruiter salary data. Most workers in this role earn between $18.08 and $21.97 per hour, depending on experience, location, and employer.

What types of projects can an entry level machine learning professional expect to work on in their first year?

As an entry-level machine learning professional, you’ll typically start by supporting more senior data scientists and engineers with tasks such as data cleaning, exploratory data analysis, and building baseline models. You may work on pilot projects like developing recommendation systems, automating simple classification tasks, or contributing to model evaluation and performance tuning. Collaboration with cross-functional teams—including software engineers, product managers, and domain experts—is common, providing valuable exposure to real-world business problems and laying a foundation for more complex responsibilities as you gain experience.

How to get into entry level machine learning with no experience?

Entry level machine learning roles typically require foundational knowledge in programming, statistics, and data analysis. Gaining skills through online courses, practicing with projects, and learning tools like Python, TensorFlow, or scikit-learn can help build a portfolio; internships or entry-level positions can provide practical experience.

What are the key skills and qualifications needed to thrive as an entry level machine learning engineer, and why are they important?

To thrive as an Entry Level Machine Learning Engineer, you need a solid background in mathematics, statistics, and programming (especially in Python), typically supported by a degree in computer science or a related field. Familiarity with machine learning frameworks like TensorFlow or PyTorch, version control systems like Git, and data analysis libraries is commonly required. Strong problem-solving abilities, curiosity, and effective communication skills help differentiate candidates in collaborative and fast-evolving environments. These skills and qualifications are essential for building, testing, and improving machine learning models that drive innovation and business value.

What is the difference between Entry Level Machine Learning vs Data Analyst?

AspectEntry Level Machine LearningData Analyst
Required CredentialsBachelor's in CS, Math, or related; some knowledge of programming and statisticsBachelor's in Statistics, Math, or related; proficiency in Excel, SQL, and data visualization tools
Work EnvironmentTech companies, startups, research labs; focus on developing models and algorithmsBusiness, finance, marketing; focus on interpreting data and generating reports
Employer & Industry UsageTech, e-commerce, healthcare; roles involve building predictive modelsRetail, finance, consulting; roles involve analyzing data trends and insights

Entry Level Machine Learning roles focus on developing algorithms and models using programming and statistical skills, often in tech-driven environments. Data Analysts interpret and visualize data to support business decisions, typically using tools like Excel and SQL. While both roles require analytical skills, Machine Learning positions emphasize coding and model development, whereas Data Analysts focus on data interpretation and reporting.

What are entry level machine learning jobs?

Entry-level machine learning jobs focus on creating and using software for the development of artificial intelligence (AI). In this role, you may help program computer software, engineer mechanical solutions, help develop learning objectives, and use analytics to determine whether or not the technology created is meeting development goals. Many entry-level machine learning jobs focus on particular parts of the industry. For example, some companies focus on surveillance and intelligence, while others are creating technology for self-driving vehicles. Employers often use this position as a type of extended learning period to help you develop your skills before you start taking responsibility for major projects.

More about Entry Level Machine Learning jobs
What job categories do people searching Entry Level Machine Learning jobs in Campbell, CA look for? The top searched job categories for Entry Level Machine Learning jobs in Campbell, CA are:
What cities near Campbell, CA are hiring for Entry Level Machine Learning jobs? Cities near Campbell, CA with the most Entry Level Machine Learning job openings:
Entry Level Machine Learning jobs near you
Infographic showing various Entry Level Machine Learning job openings in Campbell, CA as of July 2026, with employment types broken down into 1% As Needed, 68% Full Time, 28% Part Time, 1% Temporary, and 2% Contract. Highlights an 86% Physical, 2% Hybrid, and 12% Remote job distribution, with an average salary of $42,045 per year, or $20.2 per hour.

Software Engineer - AI & Edge Kubernetes Orchestration - San Jose, CA

ZEDEDA

San Jose, CA • On-site

Full-time

This job post has expired 1 day ago. Applications are no longer accepted.

Job description

Job Summary:
ZEDEDA unlocks the value of AI where it matters most, enabling enterprises to create, secure and operate edge AI at scale. They are seeking a curious, self-driven entry level Software Engineer to work on real-world problems in edge orchestration, collaborating with experienced engineers and contributing to the development of software components that bridge AI model lifecycle management with Kubernetes-based edge orchestration.
Responsibilities:
• Design, develop, and maintain software components that bridge AI model lifecycle management with Kubernetes-based edge orchestration.
• Build and extend Kubernetes controllers, operators, and Custom Resource Definitions (CRDs) to support AI workload scheduling and deployment at the edge.
• Work with ONNX, GenAI, and ML models — integrating them into production-ready pipelines and edge environments.
• Use AI coding agents (Claude Code, Copilot, Codex, etc.) as first-class tools in your daily development workflow.
• Participate in design discussions, write clean code, submit pull requests, and iterate rapidly based on feedback.
• Contribute to open-source components related to ZEDEDA's platform and the broader cloud-native ecosystem.
• Write and maintain Helm charts for deploying services into Kubernetes clusters.
• Collaborate with cross-functional teams across AI, infrastructure, and product to ship features end-to-end.
Qualifications:
Required:
• Bachelor's or Master's degree in Computer Science, AI/ML, or a related technical field — or equivalent practical experience.
• Foundational knowledge of machine learning concepts: neural networks, deep learning, model training and inference, and attention mechanisms (self-attention / transformers).
• Familiarity with ONNX models, GenAI model architectures, or frameworks like PyTorch or TensorFlow.
• Practical exposure to Kubernetes — understanding of pods, deployments, services, namespaces, and controllers. Familiarity with lightweight Kubernetes distributions such as k3s is a plus, particularly in the context of resource-constrained edge environments.
• Comfort working with Git, submitting pull requests, reading diffs, and collaborating in a version-controlled environment.
• Ability to work with vague or evolving problem statements and drive toward clarity independently.
• Language-agnostic development mindset — you pick the right tool for the job and learn what you don't know.
• Comfortable with basic Linux commands and shell scripting.
Preferred:
• Hands-on experience with Kubernetes advanced constructs: Custom Resource Definitions (CRDs), Operators, Controllers, and the kubeconfig API.
• CKA (Certified Kubernetes Administrator) or CKD certification, or active preparation for it.
• Experience with AI agent frameworks: LangChain, LangGraph, LangFuse, or similar.
• Demonstrated use of AI coding tools (Claude Code, GitHub Copilot, OpenAI Codex) in real development workflows — not just familiarity, but fluency.
• Prior contribution to, or porting of, open-source projects.
• Experience with CI/CD systems: Jenkins, CircleCI, GitHub Actions, or similar.
• Familiarity with AWS or Azure tooling.
• Knowledge of cloud-native technologies: Kafka, REST APIs, SSO/OAuth, microservices patterns.
• Exposure to Helm chart authoring, not just usage.
• Awareness of edge computing concepts, IoT, or distributed systems.
• Familiarity with edge AI hardware platforms and inference infrastructure: NVIDIA Jetson (Jetpack SDK), Qualcomm IQ9, NVIDIA Triton Inference Server, vLLM, or similar model serving frameworks.
• Familiarity with ArgoCD or other GitOps-based continuous delivery tools for Kubernetes.
Company:
ZEDEDA is a provider of distributed orchestration and virtualization software for Edge AI Compute. Founded in 2016, the company is headquartered in San Jose, USA, with a team of 51-200 employees. The company is currently Growth Stage.

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