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Project Lion - Prompt Engineer - United States (Remote, Part-Time)
Weloglobal · United States · Remote · Active · Lever
Job facts
| Field | Value |
|---|---|
| Company | Weloglobal |
| Title | Project Lion - Prompt Engineer - United States (Remote, Part-Time) |
| Normalized title | - |
| Department / team | Welo Data - AI Services / Data Validation |
| Location | United States |
| Work model | Remote / Remote |
| Employment type | Remote |
| Salary | - |
| Status | active |
| ATS provider | Lever |
| Posted / first seen | 2026-03-18 / 2026-05-29 |
| Changed / last seen | 2026-05-29 / 2026-06-06 |
Related slices
| Page | What it contains | Open |
|---|---|---|
| Company jobs | Active postings from Weloglobal. | Open |
| Company breakdowns | Role, location, ATS, and work model facets for this company. | Open |
| ATS provider jobs | Active postings observed through Lever. | Open |
| Provider filtered search | The same provider as a filtered job collection. | Open |
| Department jobs | Active postings in Welo Data - AI Services. | Open |
| Work model jobs | Active Remote postings. | Open |
| Lifecycle events | Open, update, close, and reopen events for this posting. | Open |
| Original posting | Canonical source or apply URL captured from the ATS. | Open |
Linked records
| Company | Weloglobal |
| Source | 2de9659a-8a12-48c4-a4ac-ed217a8126d1 |
| ATS provider | Lever |
Description
We are seeking a Prompt Engineer to be responsible for the end-to-end technical migration workflow for transitioning templates to LLM autoraters. The role is required to use client’s internal tools to leverage prompt engineering techniques to maximize model performance.
Federal Law Compliance
In compliance with federal law, all persons hired will be required to:
- Verify identity and eligibility to work in the United States; and
- Complete a required employment eligibility verification form.
Please note that in order to verify work authorization as is required by Federal law (I-9 process), all new employees must complete a live video verification with their selected IDs and provide photos of these selected IDs within their first 3 days of employment.
To know more details (Click here)
Responsibilities:
Utilize Automatic Prompt Generation (APG) tools to create baseline prompts for complex parent-child template clusters.
Run and supervise Automated Prompt Optimization (APO) tool, review the outputs, and flag when the APO reaches deadlocks or plateaus.
Manually draft, test, and refine prompts to navigate complex template architectures, overcome anti-patterns, and handle edge cases where tooling is lacking or broken. Solve edge-case scenarios by designing and refining manual prompts.
Monitor shadowbot runs to ensure sufficient disagreements (between human and LLM ratings) are registered, generated, and tracked.
Run prompt versions against established gold data to continuously measure autorater quality against the human crowd baseline, calculating accuracy metrics such as F1 scores, precision, and recall.
Draft technical launch readiness justifications (Launch Certification Documentation) for final.
Requirement:
Language Skills : Native fluency in English.
Location: Must be based in United States.
Education : Bachelor’s, Master’s, or Doctorate degree in Computer Science, Data Science, Computational Linguistics, Human-Computer Interaction (HCI), Cognitive Science, or a related analytical field.
Prompt Engineering & AI Expertise : At least 2 years' experience as Prompt Engineer. Proven experience tuning Large Language Models (LLMs) for strict, structured outputs, complex classification tasks, and familiarity with chain-of-thought and few-shot learning.
Data Analysis : Strong proficiency in identifying error patterns, analyzing model performance, and using SQL or other data analytics tools.
Technical Agility : Ability to quickly learn and master proprietary tools with minimal supervision.
Communication : Excellent verbal and written communication skills.
Optional / Preferred Skills:
Familiarity with enterprise-grade LLM interfaces like the Goose API.
Experience in AI model evaluation, data science, computational linguistics, or software engineering.
Hands-on experience with Automated Prompt Optimization (APO) systems or tuning workflows.
Linguistic expertise, including an understanding of semantics and logic.
Full job record
| Job ID | e815555128f3b10c8e540a9b92de3ecb4b3f7e5b |
| Org ID | aaa67d5b-8ec7-460b-a038-61d104e5a3fa |
| Source ID | 2de9659a-8a12-48c4-a4ac-ed217a8126d1 |
| Board ID | 2de9659a-8a12-48c4-a4ac-ed217a8126d1 |
| Provider | lever |
| Provider Job Key | 5fcbfe45-e8b9-4ee1-9a7e-eb1df10dee33 |
| Title | Project Lion - Prompt Engineer - United States (Remote, Part-Time) |
| Normalized Title | — |
| Status | active |
| Active | yes |
| Location Text | United States |
| Department | Welo Data - AI Services |
| Team | Data Validation |
| Employment Type | Remote |
| Workplace Type | remote |
| Remote Policy | remote |
| Country | United States |
| Region | — |
| City | — |
| Salary Raw | — |
| Salary Min | — |
| Salary Max | — |
| Salary Currency | — |
| Salary Period | — |
| Source URL | https://jobs.lever.co/weloglobal/5fcbfe45-e8b9-4ee1-9a7e-eb1df10dee33 |
| Apply URL | https://jobs.lever.co/weloglobal/5fcbfe45-e8b9-4ee1-9a7e-eb1df10dee33/apply |
| First Seen At | 2026-05-29 06:57:57Z |
| Last Seen At | 2026-06-06 19:47:59Z |
| Last Checked At | 2026-06-06 19:47:59Z |
| Last Changed At | 2026-05-29 06:57:57Z |
| Inactive At | — |
| Source Posted At | 2026-03-18 01:56:21Z |
| Source Updated At | — |
| Raw Payload Uri | s3://job-postings-prod-raw-590183727216/raw/provider=lever/board=weloglobal/date=2026-06-06/2026-06-06T19-47-57-590Z-a8314e8556cf13e2132f166d6aa55bcdff4868d19f6a4b0a04468a0297f7ea58.json |
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"text": "Responsibilities: ",
"content": "<div>\n<div>\n<ul role=\"list\" style=\"list-style-type: disc;\">\n<li aria-setsize=\"-1\" data-leveltext=\"\" data-font=\"Symbol\" data-listid=\"2\" data-list-defn-props=\"{"335552541":1,"335559685":720,"335559991":360,"469769226":"Symbol","469769242":[8226],"469777803":"left","469777804":"","469777815":"multilevel"}\" data-aria-posinset=\"1\" data-aria-level=\"1\" role=\"listitem\">\n<p><span data-contrast=\"auto\">Utilize Automatic Prompt Generation (APG) tools to create baseline prompts for complex parent-child template clusters.</span><span data-ccp-props=\"{}\"> </span></p>\n</li>\n\n</ul></div>\n<div>\n<ul role=\"list\" style=\"list-style-type: disc;\">\n<li aria-setsize=\"-1\" data-leveltext=\"\" data-font=\"Symbol\" data-listid=\"2\" data-list-defn-props=\"{"335552541":1,"335559685":720,"335559991":360,"469769226":"Symbol","469769242":[8226],"469777803":"left","469777804":"","469777815":"multilevel"}\" data-aria-posinset=\"2\" data-aria-level=\"1\" role=\"listitem\">\n<p><span data-contrast=\"auto\">Run and supervise Automated Prompt Optimization (APO) tool, review the outputs, and flag when the APO reaches deadlocks or plateaus. </span><span data-ccp-props=\"{}\"> </span></p>\n</li>\n\n</ul></div>\n<div>\n<ul role=\"list\" style=\"list-style-type: disc;\">\n<li aria-setsize=\"-1\" data-leveltext=\"\" data-font=\"Symbol\" data-listid=\"9\" data-list-defn-props=\"{"335552541":1,"335559685":720,"335559991":360,"469769226":"Symbol","469769242":[8226],"469777803":"left","469777804":"","469777815":"multilevel"}\" data-aria-posinset=\"1\" data-aria-level=\"1\" role=\"listitem\">\n<p><span data-contrast=\"auto\">Manually draft, test, and refine prompts to navigate complex template architectures, overcome anti-patterns, and handle edge cases where tooling is lacking or broken. Solve edge-case scenarios by designing and refining manual prompts.</span><span data-ccp-props=\"{}\"> </span></p>\n</li>\n\n</ul></div>\n<div>\n<ul role=\"list\" style=\"list-style-type: disc;\">\n<li aria-setsize=\"-1\" data-leveltext=\"\" data-font=\"Symbol\" data-listid=\"6\" data-list-defn-props=\"{"335552541":1,"335559685":720,"335559991":360,"469769226":"Symbol","469769242":[8226],"469777803":"left","469777804":"","469777815":"multilevel"}\" data-aria-posinset=\"1\" data-aria-level=\"1\" role=\"listitem\">\n<p><span data-contrast=\"auto\">Monitor shadowbot runs to ensure sufficient disagreements (between human and LLM ratings) are registered, generated, and tracked. </span><span data-ccp-props=\"{}\"> </span></p>\n</li>\n\n</ul></div>\n<div>\n<ul role=\"list\" style=\"list-style-type: disc;\">\n<li aria-setsize=\"-1\" data-leveltext=\"\" data-font=\"Symbol\" data-listid=\"7\" data-list-defn-props=\"{"335552541":1,"335559685":720,"335559991":360,"469769226":"Symbol","469769242":[8226],"469777803":"left","469777804":"","469777815":"multilevel"}\" data-aria-posinset=\"1\" data-aria-level=\"1\" role=\"listitem\">\n<p><span data-contrast=\"auto\">Run prompt versions against established gold data to continuously measure autorater quality against the human crowd baseline, calculating accuracy metrics such as F1 scores, precision, and recall. </span><span data-ccp-props=\"{}\"> </span></p>\n</li>\n\n</ul></div>\n<div>\n<ul role=\"list\" style=\"list-style-type: disc;\">\n<li aria-setsize=\"-1\" data-leveltext=\"\" data-font=\"Symbol\" data-listid=\"8\" data-list-defn-props=\"{"335552541":1,"335559685":720,"335559991":360,"469769226":"Symbol","469769242":[8226],"469777803":"left","469777804":"","469777815":"multilevel"}\" data-aria-posinset=\"1\" data-aria-level=\"1\" role=\"listitem\">\n<p><span data-contrast=\"auto\">Draft technical launch readiness justifications (Launch Certification Documentation) for final.</span><span data-ccp-props=\"{}\"> </span></p>\n</li>\n\n</ul></div>\n</div>"
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"text": "Optional / Preferred Skills: ",
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