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At European Recruitment, our sectors cover a wide
range of industries within the field of technology
At European Recruitment, our sectors cover a wide
range of industries within the field of technology
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At European Recruitment, our sectors cover a wide range of industries within the field of technology
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At European Recruitment, our sectors cover a wide range of industries within the field of technology
Member of Technical Staff, Machine Learning
Member of Technical Staff, Machine Learning
As a Member of Technical Staff, Machine Learning, you will build core ML components. You will work on real production systems from day one, learning how large-scale ML behaves outside of research settings.
This role is for engineers who want to develop strong systems judgment by shipping, debugging, and iterating on real-world ML.
Focus
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Build and improve ML components across data, training, evaluation, and inference.
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Fine-tune and adapt models as part of larger production systems.
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Implement evaluation and testing to understand model behavior.
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Help build and maintain data pipelines for real-world and synthetic data.
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Debug model issues, performance problems, and production incidents.
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Ship improvements iteratively and learn from real user feedback.
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Work closely with senior ML engineers and product teams.
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Work under real production constraints: latency, cost, reliability, and safety
Tech Stack
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Python
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PyTorch / JAX
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Production ML systems running on GPUs
Ideal Experience
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Strong foundations in machine learning and modern neural architectures.
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Some hands-on experience training, fine-tuning, or deploying ML models.
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Comfortable writing production-quality code and learning new tools quickly.
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Curious, coachable, and eager to learn from real systems in production.
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Able to work through ambiguity with guidance and grow ownership over time.
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Bias toward shipping, iteration, and continuous improvement.
Outcomes
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ML models in production meet expected accuracy, latency, and reliability targets.
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Production issues are identified quickly, debugged effectively, and root causes addressed.
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Data pipelines, training loops, and inference systems are robust, reproducible, and maintainable.
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Collaborates effectively with engineers, product, and research teams to deliver reliable ML-powered features.
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Iterations on models and systems are driven by real-world signals and measurable improvements.
Apply Now
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