AI Research Engineer
Hover or tap a row for full statistics (EUR / month on this chart).
Salary analysis
Compared with the selected benchmark ("All roles in Seattle, United States"), this listing's salary midpoint is about 92% lower. The offer sits below the benchmark range (€8,681–€13,311). Range-width comparison is limited because one of the salary bands is incomplete. This benchmark is based on 2 comparable listings.
| Market | Lower bound (25th percentile) | Median | Upper bound (75th percentile) |
|---|---|---|---|
| All roles in Seattle, United States | €8,681/per month | €10,996/per month | €13,311/per month |
Key Responsibilities: Design, build, and iterate on experimental ML pipelines supporting foundational model development. Implement and train large-scale models, including transformers and diffusion-based architectures, for generative and control tasks. Develop and evaluate reinforcement learning algorithms and frameworks for autonomous behaviors. Rapidly prototype and deploy research ideas into working code to accelerate AI experimentation cycles. Collaborate with cross-functional teams to integrate ML components into real-world systems. Stay at the forefront of the latest AI research and share insights internally and externally.
Job Details
Responsibilities
- Design, build, and iterate on experimental ML pipelines supporting foundational model development
- Implement and train large-scale models, including transformers and diffusion-based architectures, for generative and control tasks
- Develop and evaluate reinforcement learning algorithms and frameworks for autonomous behaviors
- Rapidly prototype and deploy research ideas into working code to accelerate AI experimentation cycles
- Collaborate with cross-functional teams to integrate ML components into real-world systems
- Stay at the forefront of the latest AI research and share insights internally and externally
Requirements
- Master’s degree in Machine Learning, AI, Computer Science, or a related technical field
- Minimum 3 years of experience in applied ML research and software engineering
- Hands-on experience training (not just using) large-scale ML models such as transformers or diffusion models
- Strong understanding of reinforcement learning fundamentals and applications
- Proficiency in Python and ML frameworks such as PyTorch, JAX, or TensorFlow
- Proven ability to write clean, efficient, and scalable code that supports fast iteration
- Excellent collaboration and communication skills
- Willing to travel occasionally
- Must have and maintain US work authorization
Skills & Technologies
Education Level
Masters
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