About Sqwish. Sqwish auto-tunes every layer of an AI stack in real time so companies optimise for business outcomes, not just latency or cost. We close the loop between production data, user behaviour and model choices, letting product teams ship faster and win in crowded GenAI markets.

We’re a fast-moving, ambitious team that cares deeply about what we build and how we build it. Speed is part of our DNA - we ship early, iterate quickly, and treat momentum as a core advantage. But we never trade quality for haste: curiosity, thoughtful decisions, and a high bar for craft guide every part of our work. We’re humble learners in a rapidly shifting field and genuinely excited to build alongside others who bring care, pace, and clarity to their craft.

Find out more at https://sqwish.ai

The problems you’ll tackle • Designing, training, and evaluating models learning from continuous data • Defining and shaping RL reward signals that reflect custom goals • Balancing experimental research with pragmatic iteration in a fast-paced product environment • Developing model evaluation strategies that go beyond static benchmarks and reflect real-world performance in dynamic systems

Core responsibilities • Propose, design, and test learning algorithms tailored to product goals and constraints • Define, shape reward functions and evaluation metrics for training workflows • Maintain strong ML research hygiene - with good notes, clear hypotheses, structured logging, organised checkpoints, and reproducible setups • Work with MLOps to deploy, monitor, and iterate on models in production environments • Analyse data and model behaviour to prioritise improvements and guide future research

We don’t expect mastery of every bullet - strength in some plus the drive to learn the rest beats a perfect checklist.

Nice to have (but teachable on the job) • Experience fine-tuning or aligning large-scale language models (LLMs or SLMs) • Familiarity with RL, reward modelling, curriculum learning, or self-supervised learning • Exposure to distributed training, model optimisation, or efficient inference • Contributions to research papers, open-source libraries, or internal tooling • Comfort working across research, product, and MLOps to move from prototype to production

What to expect: $40k-100k (+equity) based on location and experience

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