AI Engineer (Synthetic Data Pipelines) (Europe) at Jobgether

, , Romania -

Full Time

Start Date

Immediate

Expiry Date

12 Feb, 26

Salary

90000.0

Posted On

14 Nov, 25

Experience

5 year(s) or above

Remote Job

Yes

Telecommute

Yes

Sponsor Visa

Skills

AI Engineering, Synthetic Data Pipelines, Machine Learning, Statistical Analysis, Experimental Design, Python, JavaScript, Git, DVC, Data Pipeline Orchestration, LLM Systems, Context Engineering, Agentic Workflows, Debugging, Documentation

Industry

Internet Marketplace Platforms

Description

This position is posted by Jobgether on behalf of a partner company. We are currently looking for an AI Engineer (Synthetic Data Pipelines) in Europe. This role offers the opportunity to work at the forefront of AI research and engineering, developing large-scale synthetic data pipelines that power advanced machine learning models. You will bridge the gap between experimental research and production-quality systems, collaborating closely with researchers and ML engineers to design reproducible, modular pipelines. The position requires strong technical skills, a scientific mindset, and the ability to optimize LLM performance through agentic workflows and context engineering. You will contribute to scalable AI systems, iterate rapidly, and maintain rigorous quality standards while navigating complex and ambiguous problems. This is a high-impact role in a remote-first environment, where your work directly shapes the next generation of AI training infrastructure. Accountabilities Design, implement, and maintain synthetic data generation pipelines for multi-modal training tasks. Evaluate pipeline output using statistical and proxy performance metrics to ensure reliability and effectiveness. Lead experiments involving large language models (LLMs), ensuring reproducibility, clarity, and rigor. Apply agentic design patterns and context engineering techniques to maximize model performance. Use modern AI tools such as Cursor, GitHub Copilot, and LLM agents to accelerate iteration, debugging, and documentation. Collaborate closely with researchers and engineers across the stack to translate experimental insights into scalable production systems. 3+ years of software engineering experience in at least one major programming language (Python or JavaScript preferred). Strong academic background with an MS or higher in Computer Science, Engineering, Mathematics, or a related scientific field. Deep familiarity with Git, DVC, shell environments, and data pipeline orchestration. Solid foundation in statistics and experimental design for ML evaluation. Experience with LLM systems, including prompt/context engineering, agentic workflows, and output optimization strategies. Familiarity with recent research on LLM training datasets and evaluation benchmarks. Ability to thrive in fast-moving environments with ambiguous requirements. Analytical, structured, and rigorous mindset, with high attention to reproducibility and scientific soundness. Competitive salary (£70K – £90K) with equity opportunities. Fully remote role with flexibility to work from anywhere in Europe. Career growth and exposure to bleeding-edge AI research and tooling. Collaborative, high-impact environment with experienced researchers and engineers. Inclusive culture valuing curiosity, iteration, and scientific rigor. Access to modern AI tools to accelerate development and experimentation. Jobgether is a Talent Matching Platform that partners with companies worldwide to efficiently connect top talent with the right opportunities through AI-driven job matching. When you apply, your profile goes through our AI-powered screening process designed to identify top talent efficiently and fairly. 🔍 Our AI evaluates your CV and LinkedIn profile thoroughly, analyzing your skills, experience, and achievements. 📊 It compares your profile to the job’s core requirements and past success factors to determine your match score. 🎯 Based on this analysis, we automatically shortlist the 3 candidates with the highest match to the role. 🧠 When necessary, our human team may perform an additional manual review to ensure no strong profile is missed. The process is transparent, skills-based, and free of bias — focusing solely on your fit for the role. Once the shortlist is completed, we share it directly with the company that owns the job opening. The final decision and next steps, such as interviews or additional assessments, are then handled by their internal hiring team. Thank you for your interest! #LI-CL1

Responsibilities

Design, implement, and maintain synthetic data generation pipelines for multi-modal training tasks. Collaborate closely with researchers and engineers to translate experimental insights into scalable production systems.