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Labelbox Jobs (NOW HIRING)

Jobs for Humanity

Speakers/Writers

Thousand Oaks, CA · On-site

$15 - $60/hr

Labelbox is the leading data-centric AI platform for building intelligent applications. Teams looking to capitalize on the latest advances in generative AI and LLMs use the Labelbox platform to ...

Jobs for Humanity

Speakers/Writers

Hillsboro, OR · On-site

$15 - $60/hr

Labelbox is the leading data-centric AI platform for building intelligent applications. Teams looking to capitalize on the latest advances in generative AI and LLMs use the Labelbox platform to ...

Jobs for Humanity

Speakers/Writers

Irvine, CA · On-site

$15 - $60/hr

Labelbox is the leading data-centric AI platform for building intelligent applications. Teams looking to capitalize on the latest advances in generative AI and LLMs use the Labelbox platform to ...

Jobs for Humanity

Speakers/Writers

Fremont, CA

$15 - $60/hr

Labelbox is the leading data-centric AI platform for building intelligent applications. Teams looking to capitalize on the latest advances in generative AI and LLMs use the Labelbox platform to ...

Jobs for Humanity

Speakers/Writers

Vancouver, WA

$15 - $60/hr

Labelbox is the leading data-centric AI platform for building intelligent applications. Teams looking to capitalize on the latest advances in generative AI and LLMs use the Labelbox platform to ...

Jobs for Humanity

Speakers/Writers

Kansas City, MO

$15 - $60/hr

Labelbox is the leading data-centric AI platform for building intelligent applications. Teams looking to capitalize on the latest advances in generative AI and LLMs use the Labelbox platform to ...

ClifyX

Python GenAI Model Evaluator

Austin, TX · On-site

Scale AI, GroundTruth, Labelbox, Prodigy. • Prompt testing tools: Weights & Biases, MLflow, OpenAI evals, LLM-as-a-judge pipelines. Company : ClifyX provides innovative business solutions which ...

Expert Technology Services

Data Annotator

Irving, TX · On-site

$109K - $132K/yr

... CVAT, Labelbox, Scale AI, Supervisely, V7, Encord). - Ability to analyze and structure complex heavy machinery interactions into clear task-action-intent-outcome flows. - Comfortable handling ...

Labelbox

Deployment Lead

San Francisco, CA · On-site

Role Overview About the Role The Deployment Lead owns the successful delivery of complex customer programs from initial scoping through steady-state execution - on time, to the quality bar, and at ...

Expert Technology Services

Data Annotator

Irving, TX · On-site

$109K - $132K/yr

... Labelbox, Scale AI, Supervisely, V7, Encord, etc.) for high-precision data annotation. - Collaborate with internal and external teams to maintain data quality standards and continuously improve ...

Expert Technology Services

Data Annotator

Irving, TX · On-site

$109K - $132K/yr

... platforms (CVAT, Labelbox, Scale AI, Supervisely, V7, Encord, etc.). - Ability to systematically break down and label complex heavy machinery actions and interactions. - Strong 3D spatial ...

Expert Technology Services

Data Annotator

Irving, TX · On-site

... Labelbox, Scale AI, Supervisely). - Strong understanding of mining operations, spatial perception, and heavy equipment mechanics. - Attention to detail, technical aptitude, and ability to handle ...

Expert Technology Services

Data Annotator

Irving, TX · On-site

... Labelbox, Scale AI, Supervisely, V7, Encord, etc.). - Ability to break down and annotate complex heavy machinery interactions into structured flows (task → action → intent → causation → ...

Satori Digital

Account Executive

San Francisco, CA · On-site

$230K - $300K/yr

Exposure to the AI/data infrastructure space preferred (e.g., Scale AI, Retool, Labelbox, Weights & Biases) * Strong command of outbound prospecting, enterprise deal structuring, and full-cycle sales ...

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Labelbox information

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How much do labelbox jobs pay per hour?

As of Aug 18, 2026, the average hourly pay for labelbox in the United States is $26.34, according to ZipRecruiter salary data. Most workers in this role earn between $15.14 and $30.77 per hour, depending on experience, location, and employer.

What is a Labelbox?

A Labelbox job is a task or project within the Labelbox platform that involves annotating, labeling, or reviewing data for machine learning models. Companies use Labelbox to manage large-scale data labeling efforts for images, videos, text, and more. Jobs can be assigned to annotators who apply labels according to predefined guidelines. These labels help train AI models by providing high-quality training data.

What are the key skills and qualifications needed to thrive in a Labelbox role?

To thrive in a Labelbox role, such as a Data Annotation Specialist or Machine Learning Data Operations, you need a keen attention to detail, basic data management skills, and a foundational understanding of labeling processes for AI datasets. Familiarity with cloud-based annotation platforms like Labelbox, as well as experience with tools such as Excel or basic image and text editing software, is highly valuable. Strong communication, adaptability, and time management skills are important for effectively handling labeling tasks within cross-functional teams. These capabilities are essential for ensuring high-quality, accurate data labeling that supports effective machine learning model development.

What are some common challenges faced when working in a data labeling role using Labelbox?

One of the main challenges in a data labeling role using Labelbox is maintaining consistency and accuracy across large datasets, especially when dealing with complex or ambiguous data. Adhering to specific labeling guidelines and quality standards can require ongoing focus and attention to detail, and occasionally you may need to collaborate with data scientists or machine learning engineers to clarify requirements. Staying organized while meeting tight project deadlines is also common, particularly when supporting fast-paced AI development cycles. However, these challenges present valuable opportunities to build technical expertise, contribute significantly to impactful AI projects, and advance your career in the rapidly growing field of machine learning operations.

How to work on Labelbox?

To work on Labelbox, familiarize yourself with their platform for data labeling and annotation tasks. Typically, you will need skills in data management, attention to detail, and experience with labeling tools or software. Training or onboarding may be provided, and roles often require collaboration within a team environment.
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Labelbox jobs near you
Infographic showing various Labelbox job openings in the United States as of August 2026, with employment types broken down into 3% Internship, 85% Full Time, 3% Part Time, and 9% Contract. Highlights an 83% Physical, and 17% Remote job distribution, with an average salary of $54,791 per year, or $26.3 per hour.

Staff ML Engineer, Agent Training & Environments

Labelbox

San Francisco, CA • On-site

Full-time

Posted 19 days ago

Job description

Role Overview

Labelbox is the RL data factory for advancing frontier agent capabilities. We build the data, environments, and evaluations that frontier labs use to train and judge their agents.

This role sits where training meets infrastructure. You will run the experiments and build the systems that run them: environments agents act in, verifiers that decide whether they succeeded, and the fine-tuning pipelines that turn that signal into a better model. We're looking for someone who does both halves - the engineering throughput of a strong platform engineer, and real depth in post-training agents.

The bar is high: engineers with strong judgment who set technical direction, turn prototypes into reliable systems fast, and are at the frontier of agent-first engineering practice.

What you'll work on
  • RL environments for agentic tasks: task definitions, tool surfaces, state and reset semantics, reward design - and the harness that runs thousands of them in parallel.
  • Verifiers and graders: programmatic checks, LLM judges, rubric pipelines, pass@k scoring. Deciding what "the agent succeeded" means, and making that judgment trustworthy at scale.
  • Fine-tuning pipelines that turn evaluation signals into measurable agent improvements - SFT and RL, from data collection through training to checkpoint evaluation.
  • Eval systems that run millions of agent trajectories to measure model and product quality.
  • Training and serving infrastructure that scales to the throughput frontier labs need: multi-launcher orchestration, long-running job fault tolerance, cost accounting.
What we're looking for

As an engineer

  • A 3+ year track record of shipping systems that customers and other engineers still rely on.
  • Exceptional throughput, without the quality tax. You ship a lot, you review a lot, and the v1 you ship becomes the foundation the rest of the team builds on.
  • Strong system and API design judgment. Hard architecture calls land with you: you make them, defend them under pressure, and update fast when someone else is right.
  • You ship production code with coding agents daily. You know where they break and what it takes to make them reliable, and you use that to move the whole team faster.
  • You build the substrate other people's work runs on - tooling, CI, harnesses, libraries - and you treat that as the job, not a distraction from it.
  • You move fast in ambiguous, startup-pace environments, with influence over authority.
  • Deep proficiency in Python, and comfort across the rest of the stack.

As an RL post-training practitioner

  • You have fine-tuned models for agentic tasks and made them measurably better. SFT plus at least one RL method (GRPO, PPO, DPO, or similar) in production.
  • You have built environments agents operate in, and you know why reward and task design is where most of the difficulty actually lives.
  • You have designed verifiers or graders for open-ended work, and you know how they get gamed.
  • You debug training runs forensically and methodically.
  • You reason about compute-economics. You know what an experiment costs, when a run is not worth finishing, and how to get the same signal for a tenth of the spend.
  • You write up what you learned so it changes what the team does next.
Nice to have
  • Experience with agent harnesses and coding agents as subjects of training and evaluation.
  • Multi-tenancy and isolation for untrusted agent execution: sandboxing, egress control, credential handling.
  • Background in production distributed systems, ML infrastructure, or data systems at scale.
  • Experience working directly with frontier labs or other highly technical customers.
Our Technology Stack

Our engineering team works with a modern tech stack designed for scalability, performance, and developer efficiency:

  • Frontend: React.js with Redux, TypeScript
  • Backend: Node.js, TypeScript, Python, some Java & Kotlin
  • APIs: GraphQL
  • Cloud & Infrastructure: Google Cloud Platform (GCP), Kubernetes
  • Databases: MySQL, Spanner, PostgreSQL
  • Queueing / Streaming: Kafka, PubSub

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