ML Engineer - Life Sciences (Early Talent)
| Branche | Zie onder |
| Dienstverband | Zie onder |
| Uren | Zie onder |
| Locatie | Purmerend |
| Opleidingsniveau | Zie onder |
| Organisatie | Nebius |
| Contactpersoon | Zie onder |
Informatie
Launch your career at the intersection of machine learning and life sciences with Nebius through this 3–6 month Early Talent Program in Amsterdam. This opportunity is designed for current university students, recent graduates, and early career professionals who want to gain hands-on experience working with advanced biological AI models and real-world machine learning systems that support cutting-edge research and production use.
As an ML Engineer – Life Sciences, you will work on improving the speed and efficiency of powerful biological AI models used for tasks such as protein folding, protein design, and large foundation modeling. These models are highly capable but often expensive and resource-intensive to run. In this role, you will help make them more practical for real-world applications by focusing on inference optimization without significantly reducing biological quality.
Your work will include profiling inference bottlenecks in selected biological models, implementing and testing optimization techniques such as quantization, pruning, and distillation, and exploring efficient attention mechanisms and architecture-level improvements. You will also build and benchmark optimized inference pipelines, evaluate trade-offs between speed, memory usage, and accuracy, and write clean, well-documented experimental code. In addition, you will share results and provide practical recommendations for deployment.
This role is ideal for candidates who are enrolled in or have completed a degree in computer science, artificial intelligence, or a related field, and who have a strong foundation in computer science and machine learning fundamentals. You should be comfortable programming in Python, familiar with deep learning frameworks, and capable of writing clean and efficient code. Strong problem-solving skills, curiosity, and an interest in life sciences are important for success in this position.
Candidates with familiarity in large language models, transformer architectures, GPU workload profiling, deep learning system optimization, model compression techniques, or distributed inference will be especially well suited for this role. Contributions to open-source machine learning projects are also a valuable plus.
During the program, you will receive mentorship from experienced professionals in AI, machine learning, and cloud infrastructure. You will gain hands-on experience with real customer workloads and production systems, while building practical skills in model optimization, benchmarking, deployment strategy, and applied research engineering.
In addition to paid compensation, Nebius offers a dynamic and collaborative work environment that values initiative, innovation, and continuous learning. High-performing participants may also have the opportunity to be considered for a full-time role after completing the Early Talent Program.
If you are excited about advancing machine learning in the life sciences and want to work on making powerful biological AI models faster, more efficient, and more usable in practice, this program offers an excellent opportunity to begin your career with real impact.
Omschrijving
Launch your career at the intersection of machine learning and life sciences with Nebius through this 3–6 month Early Talent Program in Amsterdam. This opportunity is designed for current university students, recent graduates, and early career professionals who want to gain hands-on experience working with advanced biological AI models and real-world machine learning systems that support cutting-edge research and production use.
As an ML Engineer – Life Sciences, you will work on improving the speed and efficiency of powerful biological AI models used for tasks such as protein folding, protein design, and large foundation modeling. These models are highly capable but often expensive and resource-intensive to run. In this role, you will help make them more practical for real-world applications by focusing on inference optimization without significantly reducing biological quality.
Your work will include profiling inference bottlenecks in selected biological models, implementing and testing optimization techniques such as quantization, pruning, and distillation, and exploring efficient attention mechanisms and architecture-level improvements. You will also build and benchmark optimized inference pipelines, evaluate trade-offs between speed, memory usage, and accuracy, and write clean, well-documented experimental code. In addition, you will share results and provide practical recommendations for deployment.
This role is ideal for candidates who are enrolled in or have completed a degree in computer science, artificial intelligence, or a related field, and who have a strong foundation in computer science and machine learning fundamentals. You should be comfortable programming in Python, familiar with deep learning frameworks, and capable of writing clean and efficient code. Strong problem-solving skills, curiosity, and an interest in life sciences are important for success in this position.
Candidates with familiarity in large language models, transformer architectures, GPU workload profiling, deep learning system optimization, model compression techniques, or distributed inference will be especially well suited for this role. Contributions to open-source machine learning projects are also a valuable plus.
During the program, you will receive mentorship from experienced professionals in AI, machine learning, and cloud infrastructure. You will gain hands-on experience with real customer workloads and production systems, while building practical skills in model optimization, benchmarking, deployment strategy, and applied research engineering.
In addition to paid compensation, Nebius offers a dynamic and collaborative work environment that values initiative, innovation, and continuous learning. High-performing participants may also have the opportunity to be considered for a full-time role after completing the Early Talent Program.
If you are excited about advancing machine learning in the life sciences and want to work on making powerful biological AI models faster, more efficient, and more usable in practice, this program offers an excellent opportunity to begin your career with real impact.
