[Hiring] Machine Learning Engineer @Bjak

Role Description

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.
• This role is for engineers who enjoy solving practical ML problems, improving model quality and shipping reliable systems used by real users.

Qualifications

• 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.

Requirements

• 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.

Benefits

• ML models in production meet expected accuracy, relevance, latency and reliability targets.
• Classification and ranking improvements deliver measurable gains in model quality.
• Evaluation pipelines identify regressions and support reliable model releases.
• Training, inference and data pipelines are robust, reproducible and maintainable.
• Model improvements are driven by real-world feedback and measurable results.
• Collaborates effectively with engineering and product teams to deliver reliable ML-powered features.

How We Work

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.

Interview Process

If there appears to be a fit, we'll reach out to schedule 3, but no more than 4 interviews.

• Applications are evaluated by our technical team members.
• Interviews will be conducted virtually and/or onsite.
• We value transparency and efficiency, so expect a prompt decision.
• If you've demonstrated the exceptional skills and mindset we're looking for, we'll extend an offer to join us.