Description:

Jobgether is seeking a Senior Machine Learning Engineer to join an innovative team focused on building and scaling automated, cloud-native solutions.
The role involves designing and implementing advanced ML models and data workflows that drive real-world impact and cost optimization.
The engineer will collaborate closely with data scientists, DevOps, and engineering teams to ensure seamless model deployment and system integration.
Responsibilities include developing and maintaining robust machine learning pipelines for training, validation, and deployment.
The engineer will collaborate cross-functionally to ensure production readiness of models and successful integration with existing systems.
The role requires leveraging cloud-native tools to optimize ML workflows and infrastructure performance.
The engineer will apply DevOps principles to streamline CI/CD processes and manage containerized environments.
Ensuring compliance with data governance and security standards is also a key responsibility.
The position encourages driving innovation by researching and applying the latest trends in ML, MLOps, and cloud technologies.

Requirements:

Candidates must have proven experience in applied machine learning and data science, with a strong portfolio of deployed projects.
Advanced proficiency in Python and experience with libraries such as pandas, scikit-learn, and PyTorch is required.
A strong command of SQL and familiarity with large-scale data processing is necessary.
Experience with cloud platforms such as AWS, GCP, or Azure is essential.
A solid understanding of DevOps tools and practices, including Docker, Kubernetes, CI/CD, and infrastructure as code, is required.
Hands-on experience building ML pipelines, from data ingestion to deployment and monitoring, is necessary.
Excellent communication skills and the ability to thrive in a collaborative team environment are essential.

Employees will work alongside highly skilled engineers and ML professionals.
The company offers a flat organizational structure with direct access to leadership.
There are short feedback loops and fast feature cycles, typically ranging from 1 to 4 weeks.
The position provides flexible working hours and a remote-first environment.
Equity options are available, allowing every team member to have a stake in the company.
Employees are allocated 10% of their time for self-learning or personal projects.
The work environment features minimal bureaucracy and maximum focus on impactful work.