8 days ago
United Kingdom (city not specified)Mid Level
Responsibilities
- Evaluate foundation models against AI phone use cases.
- Determine when model adaptation or fine-tuning is needed.
- Experiment with SFT, LoRA/QLoRA, and other lightweight fine-tuning techniques.
- Improve intent recognition, instruction following, tool calling, structured outputs, and multi-step task execution.
- Explore synthetic data generation and construct datasets for phone-specific workflows.
- Build evaluation datasets and benchmarks for model quality, reliability, and task completion.
- Compare models and inference approaches, including local and cloud inference.
- Partner with the Agent Runtime team to improve model performance in agent workflows.
- Run experiments, analyze model failures, and translate findings into measurable improvements.
Requirements
- Strong background in machine learning, deep learning, or NLP.
- Practical experience with LLMs and open-weight models.
- Experience with model fine-tuning, including SFT, LoRA/QLoRA, and preferably reinforcement learning or preference optimization.
- Experience with PyTorch and common machine learning tooling.
- Understanding of LLM evaluation, benchmarking, and dataset preparation.
- Experience with agentic systems, function calling, and tool-use training is preferred.
- Experience with synthetic data generation and preference datasets is preferred.
- Experience with model distillation and smaller language models is preferred.
- Experience deploying models in production environments is preferred.
Tech Stack
Categories
About Bjak
Bjak builds an online insurance marketplace for consumers in Malaysia and Southeast Asia, enabling comparison and purchase of car, health, life, and travel policies. Its business model is aggregator-based, earning commissions and fees from insurers and partners while streamlining digital applications and renewals. Founded in 2019 and headquartered in Selangor, it is privately held and is expanding from insurance comparison into broader fintech services via a planned finance super app.