Applied Research Engineer at Labelbox

San Francisco, California, United States

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Not SpecifiedCompensation

Senior (5 to 8 years)Experience Level

Full TimeJob Type

UnknownVisa

Artificial Intelligence, TechnologyIndustries

Requirements

Expertise in machine learning and frontier model training
Deep knowledge of advanced human data alignment techniques, such as Reinforcement Learning from Human Feedback (RLHF), Direct Preference Optimization (DPO), and novel approaches
Ability to design and implement systems for creating, analyzing, and leveraging high-quality human-in-the-loop data
Skills in developing techniques to measure and improve human data quality
Experience in creating AI-assisted tools for data labeling, including active learning and adaptive sampling
Capability to investigate impacts of different human feedback types (e.g., demonstrations, preferences, critiques) on model performance and alignment
Proficiency in developing novel algorithms for optimizing human feedback collection
Ability to integrate research breakthroughs into scalable product solutions
Engagement with customers and AI community to understand data needs and share best practices
Commitment to continuous learning and staying ahead of AI advancements
Experience contributing to AI research ecosystem (e.g., publishing in top-tier journals, presenting at conferences)

Responsibilities

Develop cutting-edge systems and methods for human-in-the-loop data in AI training processes like RLHF and DPO
Design and deploy systems to measure and enhance human data quality
Create AI-assisted tools using active learning and adaptive sampling to improve data labeling efficiency and accuracy
Investigate how various human feedback types impact model performance and alignment
Develop algorithms to optimize human feedback collection for better model adaptability
Integrate research breakthroughs into Labelbox’s product suite for scalability
Engage with customers and AI community to identify evolving data needs and share best practices
Publish in top-tier journals, present at leading conferences, and contribute to human-centric AI research

Skills

Key technologies and capabilities for this role

AI DevelopmentData LabelingAnnotation ToolsWorkflow AutomationQuality ControlHuman-in-the-LoopApplied ResearchMachine Learning

Questions & Answers

Common questions about this position

What is the salary or compensation for the Applied Research Engineer role?

This information is not specified in the job description.

Is this Applied Research Engineer position remote or does it require office work?

This information is not specified in the job description.

What skills are required for the Applied Research Engineer role?

The role requires expertise in machine learning, frontier model training, and advanced human data alignment techniques such as Reinforcement Learning from Human Feedback (RLHF) and Direct Preference Optimization (DPO).

What is the company culture like at Labelbox?

Labelbox offers a high-impact environment like an early-stage startup, emphasizing impact over process, technical excellence, innovation at speed, continuous growth, and clear ownership with autonomy.

What makes a strong candidate for the Applied Research Engineer position?

Strong candidates demonstrate high agency, rapid execution, ownership, and a drive for continuous learning to solve complex AI problems at the frontier.

Labelbox

Provides data labeling solutions for AI

About Labelbox

Labelbox offers data labeling solutions for artificial intelligence applications, enabling businesses to label images, videos, text, and documents efficiently. Their platform allows users to create workflows that manage labeling tasks, which is crucial for industries like agriculture and healthcare that require large-scale data labeling for AI model training. Operating on a software-as-a-service (SaaS) model, Labelbox generates revenue through subscription fees and additional workforce services. The company's goal is to enhance AI development by providing high-quality data labeling solutions that streamline workflows.

San Francisco, CaliforniaHeadquarters

2018Year Founded

$183.7MTotal Funding

SERIES_DCompany Stage

Enterprise Software, AI & Machine LearningIndustries

201-500Employees

Benefits

Competitive remuneration

Flexible vacation policy (we don't count PTO Days)

401k Program

College savings account

HSA

Daily lunches paid for by the company (especially convenient while working from home)

Virtual wellness and guided meditation programs

Dog-friendly office

Regular company social events (happy hours, off-sites)

Professional development benefits and resources

Remote friendly (we hire in-office and remote employees)

Risks

Competition from Google's Gemini platform may attract potential Labelbox clients.

Rapid AI advancements by tech giants could outpace Labelbox's current offerings.

Reliance on partnerships like Google Cloud poses risks if these change or dissolve.

Differentiation

Labelbox offers advanced data labeling solutions for AI applications across multiple industries.

The platform supports complex NLP use cases, attracting tech and communication sectors.

Labelbox's SaaS model includes workforce augmentation services for scalable data labeling.

Upsides

Integration with Google Cloud enhances Labelbox's AI capabilities and client offerings.

Auto-computed metrics reduce error correction time and improve model performance.

Opening a London office facilitates European market expansion and better client service.

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