Precica Limited
Machine Learning Engineer
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Company Description
Precica (www.precica.com) is a deep-tech spinout of the University of Manchester, backed by Northern Gritstone and Deeptech Labs. Our product, pdata, compresses data by orders of magnitude whilst keeping it directly queryable that has applications in analytics, ML, querying and forecasting. We are running live pilots in electricity markets and expanding into unstructured data (video, logs, sensor streams). Precica is an early-stage company with a small founding team, tackling one of the defining infrastructure problems of the coming decade: the cost, energy, and carbon footprint of dark data.
Role Description
We are seeking a Machine Learning Engineer with a focus on neural and learned data compression. The role involves designing, implementing, and optimising compression models that underpin pdata's core engine. Responsibilities include:
- Developing and benchmarking compression pipelines for structured and unstructured data
- Performing statistical evaluation of rate–distortion and query-accuracy trade-offs
- Deploying models into production and pilot environments
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You will work directly with the founding team to translate research advances into scalable product capability, maintain code quality and reproducibility, and contribute to technical documentation and white papers.
As one of the first technical hires, you will have unusual scope to shape the company's technical direction. Responsibility at Precica grows with contribution and with the company itself — those who build the core of the product will be well placed to lead as we scale.
Qualifications
- Strong foundation in Computer Science: data structures, algorithms, and software engineering principles.
- Applied expertise in machine learning, with hands-on experience building and tuning models on real-world data.
- In-depth understanding of neural architectures relevant to compression and representation learning (e.g. autoencoders, transformers, CNNs, sequence models); direct experience with neural or learned compression (e.g. entropy coding, quantisation-aware training, rate–distortion optimisation) is a strong advantage.
- Proficiency in statistics, probability, and experimental design for rigorous model evaluation.
- Fluency in Python and modern ML frameworks (PyTorch, TensorFlow, scikit-learn).
- Experience with large datasets, data pipelines, and version control (Git).
- Ability to communicate complex technical concepts clearly and document work thoroughly.
- Degree in Computer Science, Mathematics, Engineering, or a related field; postgraduate research experience in ML, information theory, or compression is an advantage.
- Familiarity with cloud or high-performance computing environments is beneficial.
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Right to work
Applicants must have the right to work in the UK. We are unable to sponsor visas for this role.
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