At Labelbox, we're building the critical infrastructure that powers breakthrough AI models at leading research labs and enterprises. Since 2018, we've been pioneering data-centric approaches that are fundamental to AI development, and our work becomes even more essential as AI capabilities expand exponentially.
About LabelboxWe're the only company offering three integrated solutions for frontier AI development:
- Enterprise Platform & Tools: Advanced annotation tools, workflow automation, and quality control systems that enable teams to produce high-quality training data at scale
- Frontier Data Labeling Service: Specialized data labeling through Alignerr, leveraging subject matter experts for next-generation AI models
- Expert Marketplace: Connecting AI teams with highly skilled annotators and domain experts for flexible scaling
- High-Impact Environment: We operate like an early-stage startup, focusing on impact over process. You'll take on expanded responsibilities quickly, with career growth directly tied to your contributions.
- Technical Excellence: Work at the cutting edge of AI development, collaborating with industry leaders and shaping the future of artificial intelligence.
- Innovation at Speed: We celebrate those who take ownership, move fast, and deliver impact. Our environment rewards high agency and rapid execution.
- Continuous Growth: Every role requires continuous learning and evolution. You'll be surrounded by curious minds solving complex problems at the frontier of AI.
- Clear Ownership: You'll know exactly what you're responsible for and have the autonomy to execute. We empower people to drive results through clear ownership and metrics.
As an Applied Research intern at Labelbox, you will design, build, and productionize evaluation and post‑training systems for frontier LLMs and multimodal models. You’ll 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), and prototype RLHF/RLAIF/RLVR/RM/DPO‑style training loops to measure and improve real‑world task and agent performance.
Your Impact- 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.
- A 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 are acceptable for intern positions).
- A deep understanding of frontier autoregressive and diffusion multimodal models, along with the 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.
- The ability to bridge research and application by interpreting new findings and translating them into functional prototypes.
- A 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.
At Labelbox Applied Research, we're committed to pushing the boundaries of AI and data-centric machine learning, with a particular focus on advancing human-AI interaction techniques. We believe that high-quality human data and sophisticated human feedback integration methods are key to unlocking the next generation of AI capabilities. Our research team works at the intersection of machine learning, human-computer interaction, and AI ethics to develop innovative solutions that can be practically applied in real-world scenarios.
Labelbox strives to ensure pay parity across the organization and discuss compensation transparently. The expected annual base salary range for United States-based candidates is below. This range is not inclusive of any potential equity packages or additional benefits. Exact compensation varies based on a variety of factors, including skills and competencies, experience, and geographical location.
- Location: Join our dedicated tech hubs in San Francisco or Wrocław, Poland
- Work Style: Hybrid model with 2 days per week in office, combining collaboration and flexibility
- Environment: Fast-paced and high-intensity, perfect for ambitious individuals who thrive on ownership and quick decision-making
- Growth: Career advancement opportunities directly tied to your impact
- Vision: Be part of building the foundation for humanity's most transformative technology
We believe data will remain crucial in achieving artificial general intelligence. As AI models become more sophisticated, the need for high-quality, specialized training data will only grow. Join us in developing new products and services that enable the next generation of AI breakthroughs.
Labelbox is backed by leading investors including SoftBank, Andreessen Horowitz, B Capital, Gradient Ventures, Databricks Ventures, and Kleiner Perkins. Our customers include Fortune 500 enterprises and leading AI labs.
Your Personal Data Privacy: Any personal information you provide Labelbox as a part of your application will be processed in accordance with Labelbox’s Job Applicant Privacy notice.
Any emails from Labelbox team members will originate from a @labelbox.com email address. If you encounter anything that raises suspicions during your interactions, we encourage you to exercise caution and suspend or discontinue communications.
Skills Required
- Ph.D. or Master's degree in Computer Science, Machine Learning, AI, or a related field (in progress acceptable)
- Strong foundation in AI and machine learning
- Deep understanding of autoregressive and diffusion multimodal models
- Experience with LLM evaluation and benchmarking
- Proficiency in Python
- Experience with deep learning frameworks (PyTorch, JAX, TensorFlow)
- Exceptional communication and collaboration skills
- Track record of publishing in AI/ML conferences
Labelbox Compensation & Benefits Highlights
The following summarizes recurring compensation and benefits themes identified from responses generated by popular LLMs to common candidate questions about Labelbox and has not been reviewed or approved by Labelbox.
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Fair & Transparent Compensation — Pay is considered competitive and clearly communicated through pay transparency, with equity included in total rewards. People are often satisfied with overall packages that combine salary, stock/equity, and benefits.
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Leave & Time Off Breadth — Time off options are broad, with unlimited PTO alongside paid sick time, volunteer time, company‑wide vacations, and bereavement leave. This breadth supports flexibility for rest and personal needs.
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Healthcare Strength — Health coverage includes medical, dental, and vision insurance with added protections like life insurance and travel support for sensitive care. Wellness resources and supportive programs further reinforce the healthcare offering.
Labelbox Insights
What We Do
Labelbox is the data factory for generative AI, providing the highest quality training data for frontier and task-specific models. Labelbox’s comprehensive platform combines on-demand labeling services with the industry-leading data labeling platform. The Boost labeling service is powered by the Alignerr community of highly-educated experts, who span all major languages and a diverse range of advanced subjects. They are available on-demand to rapidly generate new data for supervised fine-tuning, RLHF, and more. Labelbox’s software-first approach delivers unmatched control and transparency into the labeling process, leading to the generation of high-quality, consistent data at scale. Customers include Fortune 500 enterprises and leading AI labs, and Labelbox is backed by leading investors including SoftBank, Andreessen Horowitz, B Capital, Gradient Ventures, Databricks Ventures, and Kleiner Perkins.
Why Work With Us
Labelboxers are driven to master their craft and build the best products for AI, which is quickly becoming one of the most significant technologies of our time. Join us in supporting our customers as they create AI breakthroughs.
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