BJAK

Machine learning engineer, applied AI

In short

This remote, full-time role involves building and deploying ML systems for AI-native applications, covering classification, ranking, retrieval, personalization and evaluation end-to-end. The company is a small, high-talent-density team building proactive AI applications that require minimal prompting. Candidates need strong ML foundations, hands-on production deployment experience, and skills in classification, ranking, retrieval or recommendation systems, with at least 2 years of relevant experience.

About the role
There are over 5 billion users using basic applications today such as email, notes, tasks, calendar and they're not AI-native. Our mission is to build proactive applications for anyone in the world, who are not used to complex prompting. We aim to bring intelligence to conversations, errands, organising and workflows, with minimal to no prompting.
Responsibilities
•Build and improve ML systems that power our AI-native applications.
•Work on classification, retrieval, ranking, personalization and evaluation, taking ownership from model development to production deployment.
•Build and improve ML models for classification, ranking, retrieval and personalization.
•Develop efficient classifiers and optimize model accuracy, precision, latency and cost.
•Improve search relevance and context retrieval using embeddings, hybrid search and reranking.
•Build personalization models that learn from user behavior while respecting privacy.
•Develop evaluation pipelines, labeled datasets and metrics to measure model quality.
•Fine-tune and adapt existing models when supported by sufficient data.
•Build and maintain ML training, inference and deployment pipelines.
•Debug production issues, monitor model performance and ship improvements based on real-world feedback.
Requirements
•Strong foundations in machine learning and modern neural architectures.
•Hands-on experience building and deploying ML models in production.
•Experience in classification, ranking, retrieval, recommendation systems or personalization.
•Understanding of model evaluation, experimentation and performance metrics.
•Comfortable writing production-quality code and working with real-world datasets.
•Able to balance model quality, latency, cost and reliability.
•Strong ownership, problem-solving skills.
Conditions
The best products in the world are built by small, world-class teams. We are a high-talent-density, hands-on team. We make decisions collectively and move quickly, balancing high-quality work with continuous learning. Joining our team requires the ability to bring structure, exercise judgment and execute independently. Our goal is to put a truly magical product into the hands of our users.

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