Machine Learning Engineer, Applied AI
At a glance
Mid-level Machine Learning Engineering role at Bjak. Remote, Switzerland · full-time.
Pay not stated Machine Learning Engineer salary
aimlroles summary, based on the employer's posting.
What you'll do
- Build classifiers, retrieval, ranking and personalization models, then take them through production deployment
- Improve classifier accuracy, precision, speed and cost while tuning search relevance with embeddings, hybrid search and reranking
- Create labeled datasets, evaluation pipelines and metrics that reveal model quality and regressions
- Maintain training, inference and release pipelines, monitor live models, and fix production problems using user feedback
- Adapt existing models when enough data is available and work with engineering and product teams on ML features
What you bring
- You have strong machine learning fundamentals and familiarity with modern neural network architectures
- You have built and launched machine learning models in production
- You bring experience in classification, ranking, retrieval, recommendation or personalization
- You can write production-grade code, work with real datasets, and weigh quality against speed, cost and reliability
Who this fits
This mid-level role suits an engineer who enjoys practical ML work and can take ownership, make sound judgments and execute independently. The position is based in Zurich and listed as remote. Interviews may take place virtually or on-site.
From the employer
About
There are over 5 billion users using basic applications today such 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.
Our first application is an agent-first mobile communication app. We believe building applications that users use daily for communication, coordination and collaboration needs will help integrate AI into their lives. The net effect is to eliminate misinformation, achieve low coordination costs, enable high quality tasks and collaboration at scale with minimal or little prompting.
Role
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.
Focus
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.
Tech Stack
Python
PyTorch / JAX
Embeddings, vector search and ranking models
Production ML systems and inference pipelines
Ideal Experience
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
Outcomes
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.