we are looking for a Machine Learning Software Engineer, who will be challenged by bridging the gap between cutting-edge machine learning research and robust production deployment.
In this position, you will combine software engineering expertise with machine learning deployment knowledge, responsible for taking our algorithms and developing robust production solutions that serve them at scale.
The work at algorithms department is fast-paced and requires staying ahead of the curve with the latest engineering solutions and best practices adopted across the ML community, while staying informed about emerging solutions in both computer vision and NLP domains and understanding the specific problems they address.
What will your job look like:
Your role will include developing production deployment systems for classical and machine learning algorithms from research and building robust, scalable inference pipelines.
You will develop primarily in Python and infrastructure tools (Kubernetes, Docker, etc.), taking part in both maintaining existing deployment systems and developing new production capabilities.
Finally, you will need to learn and implement new deployment technologies and best practices that can address emerging production challenges as they arise, while staying current with the latest MLOps and inference optimization techniques.
In this position, you will combine software engineering expertise with machine learning deployment knowledge, responsible for taking our algorithms and developing robust production solutions that serve them at scale.
The work at algorithms department is fast-paced and requires staying ahead of the curve with the latest engineering solutions and best practices adopted across the ML community, while staying informed about emerging solutions in both computer vision and NLP domains and understanding the specific problems they address.
What will your job look like:
Your role will include developing production deployment systems for classical and machine learning algorithms from research and building robust, scalable inference pipelines.
You will develop primarily in Python and infrastructure tools (Kubernetes, Docker, etc.), taking part in both maintaining existing deployment systems and developing new production capabilities.
Finally, you will need to learn and implement new deployment technologies and best practices that can address emerging production challenges as they arise, while staying current with the latest MLOps and inference optimization techniques.
Requirements:
B.Sc. in Computer Science, Software Engineering, or related technical field.
2+ years of experience in production software development, preferably in ML deployment.
Strong problem-solving skills and ability to tackle complex, real-world production challenges.
Proficiency in Python and experience with containerization and orchestration technologies (Docker, Kubernetes)- advantage.
Hands-on experience with model serving frameworks and inference optimization- advantage.
Background in distributed systems and cloud infrastructure- advantage.
B.Sc. in Computer Science, Software Engineering, or related technical field.
2+ years of experience in production software development, preferably in ML deployment.
Strong problem-solving skills and ability to tackle complex, real-world production challenges.
Proficiency in Python and experience with containerization and orchestration technologies (Docker, Kubernetes)- advantage.
Hands-on experience with model serving frameworks and inference optimization- advantage.
Background in distributed systems and cloud infrastructure- advantage.
This position is open to all candidates.
