8 days ago
USA (city not specified)Mid Level
Responsibilities
- Evaluate Qwen, Llama, Gemma, and other open-weight foundation models for phone use cases.
- Identify opportunities for model adaptation and fine-tuning.
- 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.
- Generate synthetic data 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.
- Collaborate with the Agent Runtime team to improve model performance in agent workflows.
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, and QLoRA; RL or preference optimization experience is preferred.
- Experience with PyTorch and common machine learning tooling.
- Understanding of LLM evaluation, benchmarking, and dataset preparation.
- Ability to run experiments, analyze model failures, and translate findings into measurable improvements.
- Experience with agentic systems, function calling, tool-use training, synthetic data generation, preference datasets, model distillation, smaller language models, or production model deployment 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.