AI Phone - Applied ML / Model Engineer
Bjak
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This role on the market
1,297 open applied roles across 169 companies are on ApplySarthi right now, most of them in Bengaluru (98), Hyderabad (15), Delhi NCR (7).
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What applied roles keep asking for: Python (67%), Machine learning (49%), LLMs (40%), Java (33%), C++ (31%), Deep learning (24%), AWS (24%), Generative AI (19%) — counted across their open postings here.
Deep learning jobs · LLMs jobs · Machine learning jobs · NLP jobs
Bjak has 312 open roles listed here.
- Compliance Director
- Head of Compliance
- Strategy & Operations Director
- General Manager
- CEO - Doit Lithuania
Counted across 14 company job boards, updated as roles open and close.
Preparing for this interview
Interviews for applied roles keep coming back to Python, Machine learning, LLMs, Java. Practise those questions before you sit with Bjak.
Questions you are likely to be asked
- Why do you want to join Bjak?
- What is your experience with LLMs? Tell me one thing you learned the hard way.
- Tell me about a time the data was messy or wrong. What did you do?
- How would you explain your model's result to someone who is not technical?
- What would you check first if a model's accuracy dropped after going live?
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Practise the AI Phone - Applied ML / Model Engineer at Bjak interview free →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 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. Find more English Speaking Jobs in United Kingdom on Arbeitnow
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Listed on arbeitnow · posted 2026-11-05. ApplySarthi collects openings and links to application pages; the role is advertised by Bjak, not by us.