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Salary
≈ $21k – $47k per year (Estimated)
Location
In office (Bangkok)
Seniority
Principal

Confirmed on the employer's own hiring board on Oct 20, 2026. First seen by Alion on Mar 6, 2026. Recruitis scores D on the Alion truth index.

Overview
Company
Impact
Profile match
Recruitis is an HR technology company that provides a cloud-based Applicant Tracking System (ATS) and recruitment automation software. The platform offers multi-posting job distribution, AI-assisted candidate evaluation, automated talent pool management, candidate experience tracking, and integrations with enterprise HRIS platforms and messaging systems. Headquartered in Prague, Czech Republic, the organization serves recruiters, HR teams, and commercial enterprises across Europe and international markets.

Who is our client?

Our client is a large-scale technology organization with a strong focus on engineering, data, and AI. They are continuing to invest in AI/ML platform capabilities that enable teams to build, operate, and scale machine learning and intelligent applications more effectively.

About the Role

This role is for a senior individual contributor who will lead the design and improvement of machine learning-powered solutions in a production environment. You will work on complex user-facing AI systems, drive experimentation and model improvement, and collaborate closely with cross-functional teams to turn ambiguous problems into practical, scalable solutions.

Responsibilities

  • Lead the development and enhancement of machine learning-based solutions for complex, real-world use cases
  • Translate open-ended product or user problems into clear data science approaches, experiments, and deliverable outcomes
  • Design, test, and refine prompts, retrieval strategies, evaluation frameworks, and model behaviors to improve quality and reliability
  • Build robust experimentation methods to assess model performance and compare alternative approaches
  • Work closely with product, engineering, analytics, and other technical teams to define priorities and drive implementation
  • Take ownership of solution quality across the lifecycle, from early exploration through deployment and iteration
  • Use data-driven methods to identify opportunities for model improvement, user impact, and system optimization
  • Contribute to the design of scalable workflows, including evaluation pipelines, monitoring approaches, and feedback loops
  • Communicate findings, trade-offs, and recommendations clearly to both technical and non-technical stakeholders
  • Support strategic decisions on model quality, scalability, performance, and long-term maintainability
  • Provide senior-level guidance in solving ambiguous AI problems with practical and measurable outcomes

Qualifications

  • Proven experience in a Staff Data Scientist, Applied Scientist, ML Engineer, or similar role, with strong ownership of end-to-end ML or LLM initiatives
  • Strong hands-on experience with machine learning platforms in production settings, including experimentation, evaluation, iteration, and quality improvement
  • Solid background in machine learning, statistics, and data science fundamentals
  • Experience applying quantitative methods to solve complex product or system problems at scale
  • Strong coding ability and practical experience working closely with software engineering teams in production environments
  • Experience building or improving systems that involve prompt design, retrieval, ranking, response quality assessment, or model evaluation
  • Strong understanding of experimentation design, success metrics, offline and online evaluation, and performance analysis
  • Comfortable working with ambiguity and shaping loosely defined problems into structured analytical approaches
  • Able to communicate complex technical ideas clearly and influence cross-functional stakeholders
  • Experience operating in fast-paced, product-driven environments with high expectations for quality and execution
  • Advanced degree in a relevant field such as Computer Science, Machine Learning, Statistics, Mathematics, Engineering, or a related quantitative discipline is preferred
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