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Why this grade This listing scored 66/100, which is a C. It lost the most ground on remote clarity. See the breakdown
- Pay transparency 25 / 25 A published salary range, worth more than any other single factor because it is what a candidate cannot find out without applying.
- Description depth 20 / 20 How much the posting actually says about the work, measured in characters of real text.
- Freshness 12 / 15 How recently it was posted. Older postings are likelier to be filled or abandoned.
- Remote clarity 8 / 15 Whether "remote" means anywhere, or is quietly restricted to one country.
- Role specificity 6 / 10 Whether the listing is tagged well enough to tell what the role actually is.
- Corroboration 5 / 10 Whether more than one source carries this listing.
-10 Ghost-job penalty — Deducted for signals that this posting may not be a real, currently-open role — staleness, repeated relisting, or talent-pool language.
Every figure above is arithmetic over the posting itself — its salary field, its text, its age, its tags and how many sources carry it. How the grades work →
Entry level Intern
Location: Remote (United States)
Work Model: Remote
Industry: AI recruiting and talent marketplace (B2B SaaS)
Compensation: $35–$50 per hour
About the Company
TalentPluto is a Y Combinator-backed startup building the hiring stack for high-growth companies. We run two connected products: a talent network that places engineering, go-to-market, and operations people into fast-moving startups, and an AI recruiting tool that lets a hiring team run candidate search, tiered enrichment, rubric-based scoring, and outbound campaigns directly from their assistant. We work with dozens of venture-backed startups across AI, developer tools, fintech, and healthcare. The team is small and founder-led, and interns work on live problems rather than practice ones.
The Opportunity
This is an applied AI internship inside a product where the model output is the product. We use LLMs to read unstructured professional histories, extract structure from them, score candidates against a role, and rank what a recruiter sees first. Every one of those steps can be confidently wrong, so the interesting work is as much about evaluating quality as it is about building the pipeline.
You will work on real LLM workflows against messy data at volume: designing prompts and extraction logic, building the evaluations that tell you whether a change actually helped, and improving ranking and retrieval where the current answer is not good enough. This is applied rather than research work, and you will be able to see the effect of what you build on what real users get.
Responsibilities
- Build and improve LLM workflows for extraction, scoring, and ranking against real product data.
- Design evaluations that measure output quality, and use them to judge whether a change is an improvement.
- Work with messy, high-volume unstructured data and turn it into reliable structure.
- Improve retrieval and ranking so the most relevant results surface first.
- Investigate incorrect or low-quality output and trace it back to its cause.
Requirements
- Strong programming fundamentals in Python, TypeScript, JavaScript, or a similar language.
- Interest in applied AI, machine learning, LLMs, ranking, retrieval, or data systems.
- Comfort working with messy data and evaluating output quality.
- Has built AI/ML, data, class, research, or side projects.
- Strong problem-solving ability and a willingness to learn quickly.
- Clear communicator who can work independently in a remote environment.
Originally posted on Himalayas
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Where this listing came from
- 19 Sep 2026 Himalayas first sighting
Seen on 1 board over 0 days.