AI Phone - Applied ML / Model Engineer
Bjak is hiring for the position of AI Phone - Applied ML / Model Engineer in United Kingdom. The listing describes it as a full-time position.
DGJ summary of this job
Our own short read of this listing, built only from the facts it gives. The employer’s original text follows further down.
Bjak is hiring for the position of AI Phone - Applied ML / Model Engineer in United Kingdom. The listing describes it as a full-time position.
The advert mentions machine learning, deep learning, PyTorch, LLM and NLP among the skills and tools for this role.
The employer does not state a salary in this listing; DGJ Insight below shows an official pay benchmark for the place where one exists.
Visa sponsorship is not mentioned. Applicants from outside United Kingdom should check the official work permit route before applying.
Dev Global Jobs found this job on Arbeitnow on Nov 5, 2026.
- Employer
- Bjak
- Location
- United Kingdom
- Job type
- Full-time
- Pay
- Not stated by the employer
- Found
- Nov 5, 2026 on Arbeitnow
- Employer text
- 290 words, about 1 min to read
- Apply only on the official page (arbeitnow.co.uk). Dev Global Jobs is not the employer and never asks for a fee.
- Mirror the skills the advert names (machine learning, deep learning, PyTorch and LLM) in your CV where you really have them; applicant tracking systems look for these words.
- Read the employer’s full text below (about 1 min) before you apply.
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DGJ Insight
Our own data about this role and place, from official statistics and our live job index.
Source: OECD Average annual wages, 2025
UN living-cost index for United Kingdom (ICSC post adjustment 76.4), as of 1 Oct 2026.
Source: ICSC post adjustment classifications (icsc.un.org)
- Skilled Worker visa: Job offer from a licensed sponsor at the required skill and salary level; English requirement
- Health and Care Worker visa: Eligible health and social care roles with a licensed sponsor
- Global Talent visa: Endorsement as a leader or potential leader in a listed field
Official routes: UK Visas and Immigration (GOV.UK)
About the role
Full text from the employer, as published on Arbeitnow, arranged into sections. Nothing added or removed.
01About
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.
02Responsibilities
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.
03Requirements
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.
04Nice 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.
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