Applied Research Intern, Labelbox
2-3 days a week in office
Data training platform for ML applications
Be an early applicant
Data training platform for ML applications
101-200 employees
Be an early applicant
2-3 days a week in office
101-200 employees
Company mission
To build the best products for humans to advance artificial intelligence.
Role
Who you are
- 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
What the job involves
- 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
- 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
Our take
Labelbox offers a web service and API helping companies better manage the process of annotating and labeling data used for machine learning (ML). This reduces costly and labor-intensive human data labeling, a major stumbling block for the increasing number of larger companies looking to deploy ML at scale.
Labelbox offers automated labeling as a means of boosting productivity, which puts it in direct competition with Scale AI, a company valued at over $7 billion. Founded in 2018, the relatively young Labelbox will have to find a compelling way of distinguishing itself, such as by addressing the ethical issues faced in data labeling, The company emphasizes that its product addresses the problem of bias by working as a sort of auditable record, and offers quality assurance systems to help identify it.
Labelbox has remained focused on streamlining its capabilities to raise its value in line with the rapidly growing market. The company's global expansion efforts have significantly widened its potential client base, the company has also collaborated with Google to make access to its platform easier.
Freddie
Company Specialist at Welcome to the Jungle
Insights
46% employee growth in 12 months
Company
Funding (last 2 of 5 rounds)
Jan 2022
$110m
SERIES D
Feb 2021
$40m
SERIES C
Total funding: $188.9m
Company benefits
- 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)
Company HQ
Mission District, San Francisco, CA
Leadership
Brian Rieger
(COO)Began as an Engineering Intern at Hudson Technologies. Co-founded Infinity Aerospace. Was an Aerodynamics Engineer at Boeing. Worked in Data Science for World Fuel Services.
Manu Sharma
(CEO)Masters in Astronautical Engineering from Stanford. Founder & CEO of Nuovo Wind. Founder & CEO of Ardulab/Infinity Aerospace. Worked at DroneDeploy for over 3 years, from Drone Engineer to Product Manager. Was a Product Manager at Planet.
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