AI Phone - Applied ML / Model Engineer
Remote · USFull-timePosted todayStill listed today
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Job overview
Bjak is building proactive AI applications and devices for everyday conversations, coordination, and workflows. The Applied ML / Model Engineer will evaluate open-weight foundation models for phone use cases, determine where adaptation is needed, and experiment with lightweight fine-tuning. The role also focuses on improving model capabilities, constructing datasets and benchmarks, comparing inference approaches, and collaborating with the Agent Runtime team on real agent workflows.
Skills & qualifications
Skills
Full job description
About 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
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Evaluate existing foundation models (e.g. Qwen, Llama, Gemma and other suitable open-weight models) against our target phone use cases.
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Identify which capabilities can be handled by existing models and where additional model adaptation or fine-tuning is needed.
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Experiment with SFT, LoRA/QLoRA and other lightweight fine-tuning techniques for specific use cases.
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Improve model performance for intent recognition, instruction following, tool calling, structured outputs, and multi-step task execution.
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Explore synthetic data generation and dataset construction for phone-specific workflows.
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Build evaluation datasets and benchmarks to measure model quality, reliability, and task completion.
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Compare different models and approaches, including local versus cloud inference.
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Work closely with the Agent Runtime team to improve model performance in actual agent workflows.
Requirements
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Strong background in Machine Learning, Deep Learning, or NLP.
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Practical experience working with LLMs and open-weight models.
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Experience with model fine-tuning, such as SFT, LoRA/QLoRA, and preferably RL or preference optimization.
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Experience with PyTorch and common ML tooling.
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Understanding of LLM evaluation, benchmarking, and dataset preparation.
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Ability to run experiments, analyze model failures, and translate findings into measurable improvements.
Nice to have
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Experience with agentic systems, function calling, and tool-use training.
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Experience with synthetic data generation and preference datasets.
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Experience with model distillation and smaller language models.
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Experience with deploying models in production environments.
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