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Entry Level Machine Learning Data Annotation Jobs in California

Skild AI

Computer Vision AI & ML Engineer

San Mateo, CA

$127K - $149K/yr

You will work across the full machine learning lifecycle-model development, data strategy ... Design labeling strategies and tooling for automated annotation, QA workflows, dataset management ...

Rule14

Data Scientist Machine Learning

Santa Monica, CA · On-site

$75K - $100K/yr

As a Data Scientist Machine Learning, you will work within a small data science team focusing on predictive modeling, natural language processing, computer vision, recommender systems, and OCR ...

Comrise

CA · On-site

$26 - $29/wk

CNC Machinist II - Relocation Packages Available! (Menomonie, WI) Comrise is currently looking for a skilled CNC Machinist II to join our partner's team in Menomonie, WI , to perform intermediate to ...

Skild AI

Computer Vision AI & ML Engineer

San Mateo, CA · On-site

$127K - $150K/yr

... machine learning lifecycle. Responsibilities : • Develop and optimize deep learning models for ... data annotation tools, dataset management, and augmentation techniques. • Familiarity with ...

Mercor

Learning Designer

San Francisco, CA · On-site

$125K/yr

... talent experts to complete data annotation and evaluation work * Transform complex task ... Centralized learning academies : * Build centralized academy programs for complex data types ...

Handshake

Machine Learning Engineer I

San Francisco, CA · On-site

$151K - $189K/yr

In 2025, we started Handshake AI and built the fastest-growing AI data business in history. We work ... About the Role Handshake is hiring an Associate Machine Learning Engineer for the Growth Relevance ...

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Entry Level Machine Learning Data Annotation information

What is the difference between Entry Level Machine Learning Data Annotation vs Entry Level Data Labeling Specialist?

AspectEntry Level Machine Learning Data AnnotationEntry Level Data Labeling Specialist
CredentialsBasic understanding of data annotation tools, no formal certification requiredSimilar; often no formal certification needed
Work EnvironmentRemote or on-site, working with AI teams and datasetsRemote or on-site, focusing on labeling data for AI/ML projects
Industry UsagePrimarily in AI, machine learning, and data science companiesUsed across tech, automotive, healthcare, and other industries
Search & Comparison IntentCommonly compared for entry-level roles in AI data prepOften compared as a similar entry-level data labeling role

Both roles involve preparing data for machine learning models, with similar entry-level requirements. The main difference lies in terminology and specific job focus, but they often overlap in skills and work environment.

What are the most commonly searched types of Machine Learning Data Annotation jobs in California? The most popular types of Machine Learning Data Annotation jobs in California are:
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Entry Level Machine Learning Data Annotation jobs near you
Infographic showing various Entry Level Machine Learning Data Annotation job openings in California as of June 2026, with employment types broken down into 1% As Needed, 80% Full Time, 17% Part Time, 1% Temporary, and 1% Nights. Highlights an 87% Physical, 3% Hybrid, and 10% Remote job distribution.

Computer Vision AI & ML Engineer

Skild AI

San Mateo, CA

$127K - $149K/yr

Other

Posted yesterday

Job description

Position Overview

We are seeking a Computer Vision AI & ML Engineer to design, build, and deploy advanced perception systems for real-world robotics and automation. You will work across the full machine learning lifecycle-model development, data strategy, evaluation, and production integration-to deliver robust, high-performance vision capabilities. This role combines applied research with hands-on engineering and offers the opportunity to influence both architecture and roadmap decisions.

Responsibilities
  • Develop and optimize deep learning models for depth estimation, object detection, segmentation, tracking, and 3D scene understanding using multi-modal sensor data.
  • Build scalable pipelines for data processing, training, evaluation, and deployment into real-world and real-time systems.
  • Design labeling strategies and tooling for automated annotation, QA workflows, dataset management, augmentation, and versioning.
  • Implement monitoring and reliability frameworks, including uncertainty estimation, failure detection, and automated performance reporting.
  • Conduct proof-of-concept experiments to evaluate new algorithms and perception techniques; translate research insights into practical prototypes.
  • Collaborate with robotics, systems, and simulation teams to integrate perception models into production pipelines and improve end-to-end performance.
Preferred Qualifications
  • Strong experience with deep learning frameworks (PyTorch, TensorFlow, or JAX).
  • Background in computer vision tasks such as detection, depth estimation, segmentation, tracking, or 3D scene understanding.
  • Proficiency in Python; familiarity with C++ is a plus.
  • Experience building training pipelines, evaluation frameworks, and ML deployment workflows.
  • Knowledge of 3D geometry, sensor processing, or multi-sensor fusion (RGB-D, LiDAR, stereo).
  • Experience with data annotation tools, dataset management, and augmentation techniques.
  • Familiarity with robotics, simulation environments (Isaac Sim, Gazebo, Blender), or real-time systems.
  • Understanding of uncertainty modeling, reliability engineering, or ML monitoring/MLOps practices.

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