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Verified by our engine · Posted 4h ago
- Posted
- 4h ago
- Workplace
- Hybrid
- Salary
- Not disclosed
- Visa sponsorship
- Not specified
Posted on 4 November 2026
Work model: Hybrid
Salary range not shared by the company
Visa sponsorship details unknown
This role has closed. It's kept as a record — see Bjak's open roles or the similar live roles below.
See Bjak's open rolesABOUT
We are reimagining the phone. AI phones need to be proactive, intuitive, intelligent and low cost. This is a mission critical for rest of the world to adopt intelligence.
There are over 5 billion people using everyday applications such as email, messaging, notes, tasks, and calendars, yet most are not AI-native. Our mission is to build proactive applications and devices that bring intelligence into daily conversations, coordination and workflows, with minimal prompting.
RESPONSIBILITIES
- Evaluate existing foundation models (e.g. Qwen, Llama, Gemma and other suitable open-weight models) against our target phone use cases.
- Identify which capabilities can be handled by existing models and where additional model adaptation or fine-tuning is needed.
- Experiment with SFT, LoRA/QLoRA and other lightweight fine-tuning techniques for specific use cases.
- Improve model performance for intent recognition, instruction following, tool calling, structured outputs, and multi-step task execution.
- Explore synthetic data generation and dataset construction for phone-specific workflows.
- Build evaluation datasets and benchmarks to measure model quality, reliability, and task completion.
- Compare different models and approaches, including local versus cloud inference.
- Work closely with the Agent Runtime team to improve model performance in actual agent workflows.
REQUIREMENTS
- Strong background in Machine Learning, Deep Learning, or NLP.
- Practical experience working with LLMs and open-weight models.
- Experience with model fine-tuning, such as SFT, LoRA/QLoRA, and preferably RL or preference optimization.
- Experience with PyTorch and common ML tooling.
- Understanding of LLM evaluation, benchmarking, and dataset preparation.
- Ability to run experiments, analyze model failures, and translate findings into measurable improvements.
NICE TO HAVE
- Experience with agentic systems, function calling, and tool-use training.
- Experience with synthetic data generation and preference datasets.
- Experience with model distillation and smaller language models.
- Experience with deploying models in production environments.
About Bjak
Bjak runs Southeast Asia's largest insurance-comparison marketplace and builds fintech products. A Malaysian company with engineering across Asia and Europe.
