Applied Research Intern at Labelbox

San Francisco, California, United States

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Not SpecifiedCompensation

InternshipExperience Level

InternshipJob Type

UnknownVisa

AI, Machine Learning, TechnologyIndustries

Requirements

Strong foundation in AI and machine learning, backed by a Ph.D. or Master’s degree in Computer Science, Machine Learning, AI, or a related field (in progress degrees acceptable for intern positions)
Deep understanding of frontier autoregressive and diffusion multimodal models, along with human and synthetic data strategies needed to optimize them
Passion and experience for LLM evaluation and benchmarking
Expertise in training data quality construction, measurement, and refinement
Ability to bridge research and application by interpreting new findings and translating them into functional prototypes
Track record of publishing in top-tier AI/ML conferences (e.g., NeurIPS, ICML, ICLR, ACL, EMNLP, NAACL) and contributing to the broader research community
Proficiency in Python and experience with deep learning frameworks like PyTorch, JAX, or TensorFlow
Exceptional communication and collaboration skills

Responsibilities

Build and own evaluation and benchmark suites for reasoning, code, agents, long-context, and V/LLMs
Create post-training datasets at scale: design preference/critique pipelines (human + synthetic), and target hard failures surfaced by evals
Experiment and prototype RLHF/RLAIF/RLVR/RM/DPO-style training loops to improve real-world task and agent performance
Land research in product: ship improvements into Labelbox workflows, services, and customer-facing evaluation/quality features; quantify impact with customer and internal metrics
Engage with customer research teams: run pilots, co-design benchmarks, and share practical findings through internal research reports, blog posts, talks, and published papers
Design, build, and productionize evaluation and post-training systems for frontier LLMs and multimodal models
Own continuous, high-quality evals and benchmarks (reasoning, code, agent/tool-use, long-context, vision-language, et al.)
Create and curate post-training datasets (human + synthetic)

Skills

LLMs

Multimodal Models

Model Evaluation

Benchmarks

Post-Training

Reasoning

Code

Agent

Tool-Use

Long-Context

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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