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LILT

The complete AI solution for enterprise translation and content creation.

Research Engineer – Evaluations, Applied AI

Research EngineerResearch EngineerContractRemoteSeniorTeam 201-500H1B SponsorCompany SiteLinkedIn

Location

Argentina

Posted

105 days ago

Salary

0

Seniority

Senior

Bachelor Degree5 yrs expExperience acceptedEnglishPythonPyTorch

Job Description

Research Engineer – Evaluations, Applied AI

LILT

• Eval Architecture & Benchmarking: Design and implement automated and human-in-the-loop evaluation frameworks to measure model performance across multiple modalities (text, code, image, etc.). • Calibration & Peer Review: Act as the Gold Standard reviewer for other engineers. You will calibrate their data generation and evaluation contributions, providing technical feedback to ensure scientific consistency and high-fidelity output. • Frontier Sample Generation: Write and refine complex prompts and golden response pairs for frontier-model training, specifically focusing on edge cases in reasoning and multilingual contexts. • Quality Control (End-to-End): Develop the logic for multi-modal QC checks, ensuring that high-volume data samples are correct across diverse domains and languages. • Technical Mentorship: Bring new knowledge and best practices to our established delivery and forward-deployed engineering teams on model evaluations.

Job Requirements

  • Education: B.S. in Computer Science, AI, or a related field or 5+ years of relevant experience in a high-growth AI/Research environment.
  • Deep Technical Proficiency: Expert-level Python skills and hands-on experience with modern AI frameworks (PyTorch, Transformers, LangChain/LlamaIndex).
  • Evaluation Experience: Experience building model evaluation suites (e.g., MMLU-style benchmarks, custom RAG metrics, or human-preference alignment).
  • Domain Expertise: Deep understanding of RAG architectures, vector database retrieval logic, and agentic workflows. Experience with RLHF/RLAIF environments and the mechanics of preference signaling/reward modeling.
  • Multimodal & Multilingual Rigor: Experience handling data quality at scale across different languages and modalities (images, video, or audio).
  • Precision- and Quality-Orientation: You find bugs in model reasoning that others miss. You are comfortable being the final quality arbiter for technical deliverables that others produce.

Benefits

  • Health insurance
  • 401(k) matching
  • Flexible work hours
  • Paid time off
  • Remote work options

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