Frontier AI ML Engineer: Code Evaluation & MLOps

Synthires; España; Presencial; EUR 83.000 - 118.000; A tiempo parcial

Ventajas ofrecidas por este puesto de trabajo

Weekly payments

Descripción de la vacante

Synthires is seeking an experienced Machine Learning Engineer to contribute to frontier AI coding model evaluation projects. You will assess AI-generated ML implementations, review training and MLOps tasks, and apply real-world ML expertise to improve next-generation AI coding agents.

The role emphasizes evaluating engineering quality, identifying issues, and providing actionable feedback while collaborating with technical reviewers to keep high evaluation standards across projects.

Formación

• 2+ years of professional Machine Learning Engineering experience.
• Experience building production ML systems, deployment infra, or AI-powered products.
• Strong programming with Python and ML frameworks.
• Ability to evaluate AI-generated ML implementations and tradeoffs.
• Excellent analytical, problem-solving, and written communication skills.

Responsabilidades

• Use frontier AI coding agents to complete and evaluate ML tasks.
• Review AI-generated implementations including training and MLOps.
• Identify bugs, edge cases, bottlenecks, and failures.
• Compare outputs from multiple AI coding models and assess strengths/weaknesses.
• Provide structured technical feedback to improve code quality and performance.
• Collaborate with reviewers to maintain evaluation standards.
• Apply professional engineering judgment to ML workflows.

Conocimientos

Python

ML workflows

MLOps

Analytical thinking

Communication

Herramientas

Cursor

Claude Code

Codex

Windsurf

Gemini CLI

Descripción del empleo

Synthires is seeking an experienced Machine Learning Engineer to contribute to frontier AI coding model evaluation projects. You will assess AI-generated ML implementations, review training and MLOps tasks, and apply real-world ML expertise to improve next-generation AI coding agents.

The role emphasizes evaluating engineering quality, identifying issues, and providing actionable feedback while collaborating with technical reviewers to keep high evaluation standards across projects.