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Edge Ai Machine Learning Jobs in California (NOW HIRING)

Veritone, Inc.

AI/Machine Learning Engineer

Irvine, CA · On-site

$175 - $200/hr

Veritone's leading enterprise AI platform, aiWARE™, orchestrates an ever-growing ecosystem of machine learning models, transforming data sources into actionable intelligence. By blending human ...

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Veritone

AI/Machine Learning Engineer

Irvine, CA · On-site

POSITION SUMMARY The AI/ML Engineer will participate in building, documenting, and refactoring ... machine learning workloads and production code. DISCLOSURE Our company provides equal employment ...

Veritone

AI/Machine Learning Engineer

Irvine, CA · On-site

POSITION SUMMARY The AI/ML Engineer will participate in building, documenting, and refactoring ... machine learning workloads and production code. DISCLOSURE Our company provides equal employment ...

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Edge Ai Machine Learning information

What is an Edge AI Machine Learning?

An Edge AI Machine Learning job involves developing and deploying machine learning models directly on edge devices, such as IoT sensors, mobile devices, and embedded systems. This role requires expertise in optimizing AI models for low-power, low-latency environments while ensuring real-time processing. Professionals in this field work with frameworks like TensorFlow Lite, ONNX, and OpenVINO to implement AI solutions efficiently. They must also handle challenges like model compression, hardware acceleration, and data privacy.

What are some typical challenges faced in an Edge AI Machine Learning role, and how can I prepare for them?

One of the most common challenges in Edge AI Machine Learning is optimizing models to run efficiently on hardware with limited resources, while maintaining acceptable accuracy and speed. You may encounter constraints related to memory, processing power, and connectivity, which require creative engineering and a deep understanding of both machine learning and embedded systems. Collaborating closely with hardware engineers, data scientists, and software developers is typical, as solutions often span multiple technical disciplines. To prepare, staying current with advancements in model compression, quantization, and edge deployment technologies will help you tackle these challenges with confidence.

What are the key skills and qualifications needed to thrive in the Edge AI Machine Learning position?

To thrive as an Edge AI Machine Learning professional, you need a strong background in machine learning algorithms, embedded systems, and proficiency with programming languages such as Python or C++. Familiarity with edge computing platforms (like NVIDIA Jetson, Google Coral), frameworks (TensorFlow Lite, ONNX), and certifications in AI or ML can greatly enhance your qualifications. Strong problem-solving abilities, collaboration, and effective communication skills are important for adapting solutions to diverse environments and working cross-functionally. These abilities enable the successful deployment of efficient and robust AI models directly on devices, meeting the unique challenges of real-time, resource-constrained settings.

How to become an edge AI machine learning engineer?

To become an edge AI machine learning engineer, develop strong skills in machine learning, embedded systems, and programming languages like Python and C++. Gain experience with hardware platforms such as NVIDIA Jetson or Raspberry Pi, and learn to optimize models for low-power, resource-constrained environments. Earning certifications in AI, embedded systems, or IoT can also enhance your qualifications.
What are the most commonly searched types of Edge Ai Machine Learning jobs in California? The most popular types of Edge Ai Machine Learning jobs in California are:
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What cities in California are hiring for Edge Ai Machine Learning jobs? Cities in California with the most Edge Ai Machine Learning job openings:
Edge Ai Machine Learning jobs near you
Infographic showing various Edge Ai Machine Learning job openings in California as of August 2026, with employment types broken down into 5% Internship, 79% Full Time, and 16% Contract. Highlights an 90% In-person, 5% Hybrid, and 5% Remote job distribution.

Machine Learning Engineer, AI Labs

Netskope

Santa Clara, CA

Full-time

Re-posted 12 days ago

Job description

About Netskope & AI Labs

Within Netskope Engineering, Netskope AI Labs is the powerhouse advancing state-of-the-art artificial intelligence (AI) and machine learning (ML) to protect the modern enterprise. We build the intelligence behind the Netskope Intelligent Security Service Edge (SSE) platform.

We are seeking a high-caliber Machine Learning Engineer to help us build, optimize, and deploy enterprise-scale AI solutions. Working closely with senior architects, you will directly influence our Secure Access Service Edge (SASE) architecture, turning cutting-edge AI research into production-grade reality.

Note on Leveling: We believe great talent doesn't always fit into a rigid box. Candidates are assessed individually and leveled (from mid to senior) according to their specific skills, background, and technical depth.

What's in it for You?
  • High-Impact Ownership: You aren't just maintaining pipelines; you are playing a critical role in the AI transformation of a market-leading cloud security company.
  • Cutting-Edge Stack: Work on the bleeding edge of LLM inference optimization, utilizing tools like vLLM, SGLang, and advanced KV Cache optimization.
  • Elite Collaboration: Work alongside top-tier engineers, researchers, and ML scientists to solve the industry's toughest challenges in latency, throughput, and cloud security.
What You Will Do
  • Collaborate on the AI Roadmap: Play a key role alongside senior architects and team members in driving the execution of critical AI/ML technical strategies, building highly scalable, reliable, and production-grade systems.
  • Architect High-Performance Inference Systems: Design, optimize, and deploy enterprise-scale LLM serving infrastructures. You will push the boundaries of throughput and latency.
  • Own the End-to-End AI Lifecycle: Partner closely with ML scientists and product stakeholders to translate complex business requirements into elegant, deployed code.
  • Enforce AI Excellence: Implement and scale strict "Report Cards" for production models, tracking real-world accuracy, latency, and security relevance.
What You Bring
  • Industry Experience: 10+ years of overall experience in software engineering and product development, with a specialized focus in one of two tracks:
    • The AI/ML Focus: 2+ years of production experience developing, optimizing, and deploying AI/ML solutions (or an equivalent blend of an advanced technical degree + hands-on experience).
    • The Distributed Systems Focus: 6+ years of deep experience architecting, building, and scaling high-performance distributed systems, combined with a strong desire to apply those infrastructure skills to cutting-edge AI/LLM engineering.
  • The Modern AI Stack: Direct exposure to (or a strong conceptual understanding of) optimizing LLMs in production. Familiarity with high-throughput inference frameworks (e.g., vLLM, SGLang, TensorRT-LLM) and memory management techniques like KV Cache optimization is a massive plus.
  • Clear Communication: The ability to distill complex technical architecture or infrastructure bottlenecks into clear, actionable concepts for cross-functional teams.
  • The Startup Mindset: You are an energetic self-starter who thrives in fast-paced, dynamic environments and isn't afraid to wear multiple hats to get a product across the finish line.
Education
  • BSCS or equivalent required, MSCS or equivalent strongly preferred.

Apply