Senior Machine Learning Engineer

Bjak seeks a Senior Machine Learning Engineer in Zürich to independently own and build critical ML subsystems for a proactive AI assistant, focusing on production reliability and end-to-end system ownership.

This position offers independent ownership over essential machine learning components within a live product environment. The role demands a hands-on approach with a strong emphasis on technical depth, requiring the engineer to translate research concepts into robust, production-ready systems that deliver tangible user value.

Responsibilities

• Develop foundational ML infrastructure supporting a forward-looking AI product.
• Manage the complete lifecycle of ML work, including data handling, model training, evaluation, deployment, and continuous refinement.
• Convert experimental research concepts into stable, operational systems.
• Identify and resolve model or system failures by analyzing live production data.
• Maintain a fast development cycle: release, assess results, adjust, and repeat.
• Partner with research, product, and engineering teams to ensure solutions meet user needs.
• Guide and evaluate the work of fellow ML engineers through technical leadership.
• Operate within strict production limits regarding latency, expenses, reliability, and safety.

Requirements

• Proven experience in building and deploying ML systems for actual users.
• Deep understanding of how modern ML models perform and fail in live environments.
• Ability to write high-quality, production-grade code with a systems-oriented mindset.
• Strong sense of ownership, self-direction, and ability to drive projects to completion.
• Fast learning ability, clear communication skills, and a commitment to iterative improvement.

About the company

Bjak is developing a proactive AI smart assistant designed to enhance everyday tasks such as conversations, errands, organization, and workflows. The product prioritizes high reliability for long-running processes, persistent context, and real-world task completion. The team operates with high talent density, makes collective decisions, and moves quickly while balancing quality and learning to create a truly magical user experience.

• High talent density with a hands-on approach.
• Collective decision-making and rapid execution.
• Balance between shipping high-quality work and continuous learning.
• Focus on delivering a magical product to users.