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Data Annotation Research Jobs in California (NOW HIRING)

Skild AI

Computer Vision AI & ML Engineer

San Mateo, CA · On-site

$127K - $150K/yr

... research insights into practical prototypes. • Collaborate with robotics, systems, and simulation ... data annotation tools, dataset management, and augmentation techniques. • Familiarity with ...

Skild AI

Computer Vision AI & ML Engineer

San Mateo, CA · On-site

$127K - $149K/yr

This role combines applied research with hands-on engineering and offers the opportunity to ... Experience with data annotation tools, dataset management, and augmentation techniques.

General Motors

Senior ML Systems Engineer

Sunnyvale, CA · On-site

$122K - $168K/yr

... data-annotation pipelines and machine-led training data solutions at foundation-model scale . We ... researchers. * Experience with computer vision, machine learning , or data-centric AI projects ...

Mercor

Learning Designer

San Francisco, CA · On-site

$125K/yr

You'll work alongside researchers, operators, and AI companies at the forefront of shaping the ... talent experts to complete data annotation and evaluation work * Transform complex task ...

Mercor

Bilingual Language Model Evaluator

San Francisco, CA · Remote

$15 - $20/hr

About the job Mercor connects elite creative and technical talent with leading AI research labs ... Prior experience with RLHF, model evaluation, or data annotation work . * Experience writing or ...

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Data Annotation Research information

What qualifications do I need for data annotation?

Data annotation research roles typically require basic computer skills, attention to detail, and familiarity with annotation tools or platforms. A high school diploma or equivalent is usually sufficient, though some positions may prefer experience with data labeling, machine learning concepts, or specific software. Strong communication skills and the ability to work independently are also beneficial.

What are some common challenges faced in Data Annotation Research roles, and how can they be addressed?

Professionals in Data Annotation Research often encounter challenges such as maintaining consistency in labeling, dealing with ambiguous data, and managing large datasets efficiently. These issues can be addressed by following detailed annotation guidelines, participating in regular calibration sessions with the team, and utilizing annotation tools that support quality control checks. Collaboration with data scientists and project managers is essential to clarify ambiguities and ensure that annotated data meets the project's requirements. Staying proactive in communication and continuous learning helps to minimize errors and improve overall data quality.

Does data annotation actually pay?

Data annotation research jobs typically pay hourly or per task rates, with wages ranging from minimum wage to higher rates depending on experience and complexity of the work. Many positions are freelance or remote, requiring basic skills in data labeling tools and attention to detail. Payment is generally reliable, but rates vary by employer and project.

How hard is it to get hired by data annotation?

Getting hired for a data annotation research role typically requires basic computer skills, attention to detail, and sometimes familiarity with annotation tools or platforms. Many positions are entry-level and do not require advanced education, making the hiring process relatively accessible for those with the right skills and reliability.

What is the difference between Data Annotation Research vs Data Labeling Specialist?

AspectData Annotation ResearchData Labeling Specialist
CredentialsTypically requires a background in data science, research methods, or related fieldsOften requires basic technical skills and experience with labeling tools
Work EnvironmentResearch labs, tech companies, or remote research teamsData centers, tech companies, or remote labeling teams
Industry UsageUsed in AI/ML research, developing annotation methodologiesUsed in preparing datasets for machine learning models
Search & Comparison IntentUnderstanding research-focused roles in data annotationLooking for practical data labeling jobs

Data Annotation Research involves exploring new annotation techniques and improving data quality for AI models, often requiring research skills. In contrast, Data Labeling Specialists focus on applying existing labeling tools to annotate datasets efficiently. Both roles are essential in AI development but differ in scope and expertise.

Is data annotation real or fake?

Data annotation is a real and essential process in machine learning and AI development, involving labeling data such as images, text, or audio to train algorithms. Data annotation jobs require attention to detail and often use tools like labeling platforms or software, making them a legitimate employment opportunity in the tech industry.

What is data annotation research?

Data annotation research involves studying and developing methods for labeling data, such as images, text, or audio, to be used in training machine learning models. Researchers in this field focus on improving annotation accuracy, efficiency, and scalability, as well as addressing challenges like bias and consistency. This work is critical because high-quality annotated data is essential for building effective AI systems. Data annotation research often includes exploring new tools, techniques, and guidelines for human annotators or automated labeling systems.

What are the key skills and qualifications needed to thrive as a Data Annotation Researcher, and why are they important?

To thrive as a Data Annotation Researcher, you need strong attention to detail, analytical thinking, and familiarity with data labeling concepts, often supported by a degree in computer science, linguistics, or a related field. Experience with annotation platforms, data management tools, and sometimes knowledge of programming languages like Python are typically required. Excellent communication, problem-solving abilities, and the capacity to work independently set standout contributors apart. These skills ensure high-quality, accurate data labeling, which is crucial for developing reliable AI and machine learning models.
What are popular job titles related to Data Annotation Research jobs in California? For Data Annotation Research jobs in California, the most frequently searched job titles are:
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What cities in California are hiring for Data Annotation Research jobs? Cities in California with the most Data Annotation Research job openings:
Data Annotation Research jobs near you
Infographic showing various Data Annotation Research job openings in California as of June 2026, with employment types broken down into 3% As Needed, 11% Full Time, 66% Part Time, 3% Temporary, 15% Contract, and 2% Nights. Highlights an 87% Physical, 3% Hybrid, and 10% Remote job distribution.

Research Engineer, Multimodal Data

Eventual Computing

San Francisco, CA • On-site

$150K - $250K/yr

Full-time

Medical, Dental, Vision, Retirement, PTO

Posted 5 days ago

Job description

About Eventual
Every breakthrough Physical AI system - humanoid robots, autonomous vehicles, video generation models - is trained on petabytes of video, lidar, radar, and sensor data. But today's data platforms (Databricks, Snowflake) were built for spreadsheet-like analytics, not the multimodal corpora that power AI. As a result, robotics and video-AI teams iterate on model improvement about once a week. Most of that week isn't training - it's finding the right data: writing CV heuristics over raw footage, paying annotators for edge cases, hand-curating clips before a cluster ever spins up. GPU bandwidth has grown 2-3× per generation. Storage and pipelines haven't. The gap widens every year.
Eventual was founded in 2022 to close it. Our open-source engine, Daft, is the distributed data engine purpose-built for multimodal AI - already running 2 PB/day at Amazon, 60-100 PB at another FAANG company, and in production at Mobileye, TogetherAI, and CloudKitchens. We are building a video-native index on top of our engine for Physical AI that collapses the data iteration loop. Describe the dataset you want, get a curated table in minutes, feed it to your GPUs at line rate. One iteration per day becomes the norm.
We're building this in partnership with the top PhysicalAI labs and public AI infrastructure companies today. We have raised $30M from Felicis, CRV, Microsoft M12, Citi, Essence, Y Combinator, Caffeinated Capital, Array.vc, and angels from the co-founders of Databricks and Perplexity. We've assembled a world-class team from AWS, Render, Pinecone and Tesla. We have spent our careers powering the last generation of PhysicalAI in self-driving, and are excited to now do this for the next.
Join our small (but powerful!) team working together 4 days/week in our SF Mission district office.
Your Role
As a Research Engineer on the Visual Understanding team, you'll own the layer that makes petabytes of video queryable by content. Physical AI teams have raw footage, lidar, radar, and sim outputs scattered across object stores with no way to find what they need without weeks of human annotation. We change that economics: we run vision-language models over every clip in a corpus along axes the customer cares about (gripper type, failure mode, object class, scene, motion density), so a researcher can ask "left-arm grasp failures on deformable objects" and get a curated dataset in minutes.
You'll define the roadmap for our visual understanding capabilities, train and select the models that make corpus-scale annotation tractable at single-digit cents per hour of video, and build the rich datasets that go on to train customer models. This is a research engineering role - meaning you'll read papers and run experiments, but you ship to production and your work is judged by what it does for customer training runs.
Key Responsibilities
  • Own the visual understanding roadmap end-to-end: from picking the model family for a customer's taxonomy to landing it in production inference at corpus scale.
  • Train, fine-tune, and evaluate VLMs, VQA models, embedding models, and convolutional perception models against customer datasets and benchmarks.
  • Drive down per-clip annotation cost - model selection, distillation, batching, decode pipelining - so "annotate every clip in a 10K-hour corpus" stays economical.
  • Build the rich, queryable datasets that customers train on: design taxonomies with researchers, instrument quality, version the outputs.
  • Partner with the dataloading and storage teams so visual understanding outputs flow into the index and on to the GPU without re-engineering.
  • Work directly with researchers at our partner labs - your shortest feedback loop is their next training iteration.

What we look for
  • Strong familiarity with modern vision and multimodal models - convolution nets, VLMs, VQA, embeddings - and a sense for the SOTA that's actually deployable today vs. on a leaderboard.
  • Experience running these models at scale on real video and sensor data, ideally for perception tasks (detection, tracking, segmentation, retrieval, captioning).
  • Background from a perception team at a self-driving, robotics, or visual-data company - or equivalent depth from a research lab.
  • Comfortable with cloud infrastructure and large-scale data processing - you don't need to be a distributed-systems engineer, but you've shipped jobs that ran on thousands of GPU-hours of video.
  • Bias toward data and infrastructure: you reach for "annotate the whole corpus" before "fine-tune another model."

Nice to have
  • Experience training vision or multimodal models from scratch (not just calling APIs).
  • ML/AI research background - papers, citations, or a research org on your resume.
  • Hands-on time with big-data frameworks like Spark, Ray, or Daft.
  • Worked on embeddings, retrieval, or content-aware search at scale.
  • Experience designing labeling taxonomies or running annotation programs.

Perks & Benefits
  • In-person, tight-knit team - 4 days/week in our SF Mission office.
  • Competitive comp and meaningful startup equity.
  • Catered lunches and dinners for SF employees.
  • Commuter benefit.
  • Team-building events and poker nights.
  • Health, vision, and dental coverage.
  • Flexible PTO.
  • Latest Apple equipment.
  • 401(k) plan with match.

If you're excited about being on the team that turns petabytes of raw video into the training data for the next generation of Physical AI, we'd love to talk.

Apply