Senior Machine Learning Engineer

Senior Machine Learning Engineer

We are looking for a Senior Machine Learning Engineer who will play a pivotal role in harnessing the power of data to drive operational excellence. You will design, develop and deploy sophisticated data models, leverage cloud-native technologies, and contribute to our DevOps and Machine Learning operations.

Responsibilities:

• Develop and maintain ML training, validation, and deployment pipelines
• Utilize cloud-native technologies to optimize data workflows and ensure seamless integration with our existing platforms
• Participate in cross-functional projects and collaborate with various teams to achieve company goals
• Collaborate with data scientists to streamline model hand-off and production readiness.
• Stay current with the latest industry trends and advancements in data science, cloud technologies, and DevOps practices
• Ensure data compliance and security measures are upheld across all operations.

Requirements:

• Proven experience in applied Data Science/Machine Learning, with a strong portfolio of projects that demonstrate your expertise
• Strong programming skills in Python (proven experience with pandas, sklearn, pytorch would be a great plus) and SQL
• Proficiency in cloud-native technologies and understanding of cloud architecture (AWS, GCP, Azure, or similar)
• Solid understanding of DevOps practices, with experience in CI/CD, infrastructure as code, containerization, and orchestration (Docker, Kubernetes, or similar)
• Familiarity with ML pipeline tools and practices, including data collection, preprocessing, model training, deployment, and monitoring
• Excellent problem-solving abilities and attention to detail
• Strong communication skills and the ability to work effectively in a team-oriented setting.

Bonus points:

• Experience with MLOps tools like MLflow, Feast, Kubeflow
• Experience with large-scale data processing tools like Ray, Spark, Apache Beam/Flink

What's in it for you?

• Team of highly skilled professionals to work with and learn from
• Impact and visibility. Our organization is flat, getting in touch with CEO or CTO is a common practice here
• Short feedback loop. We have an obsession with customer satisfaction. The ship features fast and gets instant feedback. Feature projects tend to be completed in 1 to 4 weeks, depending on the scope
• Flexible working hours. We deliver instead of sitting in the office 8 to 5
• Skin in the game. Every employee gets a share of the company
• Time to focus on work with a minimum overhead of meetings, bureaucracy, etc.
• 10% time to focus on self-improvement or personal projects.