Description review

Copy of AI Code Reviewer & Systems Evaluation Engineer 2

Lifted (an Upwork Company) · Thailand · back to the listing

HR standards

63/100

needs work

Title ↔ description

52/100

needs work

Reads as

Backend Engineer

85% confident

What this role officially is

software developer — ESCO, the EU occupation classification

Software developers implement or program all kinds of software systems based on specifications and designs by using programming languages, tools, and platforms.

Also known as: application developer, application programmer, solutions developer, programmer, software specialist, application software developer

How others title the same work

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  • Golang Engineer Canonical Ltd.

Startups

  • Anterior (Sequoia-backed, Series B) | Senior Member of Technical Staff | On-Site (New York, NY) | Full Time | $230,000–$300,000 + equity Anterior (Sequoia-backed, Series B)
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  • Distributed Systems Engineer Beam
  • Software Engineer, Platform Beam
  • Blaine, WA | CaseLight Systems Inc. (CSI) | Solo Founder | Remote (US Only) | Founding Systems Engineer | FULL EQUITY (30% Stake) Blaine, WA

What the listing never says

  • 23 bullet points. Long requirement lists deter qualified candidates, who read them as hard gates. Scope clarity
  • No pay range published. Candidates cannot tell whether applying is worth their time. Pay transparency

The listing, marked up

Nothing in the wording of this listing tripped a check. The scores above still judge how complete and coherent it is.

This opportunity is ideal for engineers who enjoy analyzing systems, improving code quality, and working on complex technical challenges. You will contribute to AI training projects by evaluating outputs, refining logic, and identifying potential vulnerabilities.

What You'll Do:

• Develop objective, verifiable evaluation criteria (rubrics) for system performance

• Review system logs and execution paths to improve reliability and code quality

• Refactor code and optimize system behavior toward ideal outcomes

• Test systems for vulnerabilities, including data exposure and edge-case failures

• Provide detailed, high-quality feedback on system performance and outputs

Requirements:

• 2+ years of experience in backend engineering, AI automation, or systems integration

• Strong proficiency in at least two programming languages (e.g., Python, JavaScript, Go, Java)

• Experience working with SQL databases

• Proven ability to build and maintain production-grade systems

• Experience working in live (non-mocked) environments with multi-step interactions

• Strong analytical skills and attention to detail

Nice to Haves:

• Experience with multi-stage system workflows and coordination tasks

• Familiarity with integrating tools such as APIs, databases, or external platforms

• Understanding of system vulnerabilities (e.g., privacy leaks, prompt injection, access escalation)

• Experience working with AI systems or agent-based workflows

• Comfort working with persistent state tracking or similar frameworks

• Fully remote and flexible work schedule

• Project-based engagement with no guaranteed hours

• Work on tasks based on availability and project assignment

• Payment is based on completed tasks only

• Must accept project invitations before beginning work

• Freelancers may accept or decline tasks depending on availability

• No guaranteed workload; volume may vary weekly

An enterprise client is currently seeking experienced software engineers to contribute to improving advanced AI systems through human feedback. This work supports leading AI organizations in training large language models to better understand software development practices, debugging, and code quality.

This is part of a cutting-edge initiative focused on enhancing how AI systems write, review, and optimize code in real-world scenarios. You’ll play a key role in shaping how AI models evaluate performance, detect issues, and generate reliable outputs.

Originally posted on Himalayas

How this was produced

Highlights are found by rule, not by a model: each one is a phrase matched at a known position, and every note is a template we wrote. The two scores come from a typed-decision model (Jev) that reads the listing against the official role definition and real listings for the same role, and returns probabilities rather than prose — it never writes any of the words on this page, and never chooses what to highlight.

Deterministic penalty applied to the HR score: 8 points (from 71 before penalties). Reviewed 1 Oct 2026.