Machine Learning Engineer
Manchester, UK · Full-time
About Precica
Who we are.
Precica (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 — with applications in analytics, ML, querying, and forecasting. We're 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.
The Role
What you'll do.
We're 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, and deploying models into production and pilot environments.
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.
Requirements
What we're looking for.
Essential
- 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.
- Proficiency in statistics, probability, and experimental design for rigorous model evaluation.
- Fluency in Python and modern ML frameworks (PyTorch, TensorFlow, scikit-learn) and adaptability to work across frameworks.
- 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.
Desirable
- Direct experience with neural or learned compression: quantisation-aware training, rate–distortion optimisation, or learned transforms.
- Postgraduate degree or research experience in machine learning, information theory, signal processing, or data compression.
- Background in low-rank or structured representations (e.g. tensor decompositions, sketching, dimensionality reduction).
- Experience with time-series modelling and forecasting, particularly in energy or industrial settings.
- Familiarity with embedding-based retrieval and video/image representation learning (e.g. CLIP-style models, vector search).
- Performance engineering skills: C++, CUDA, or optimisation of inference pipelines for throughput and memory.
- Experience deploying models to production (MLOps, containerisation, CI/CD) and with cloud or HPC environments.
- Publications, open-source contributions, or patents in a relevant area.
Logistics
A few practical details.
- Minimum education: Bachelor's degree or an equivalent combination of education, training, and/or experience.
- Required field of study: Computer Science, Mathematics, Engineering, or a related field relevant to the role.
- Minimum years of experience: This is an entry-level role — we weigh demonstrated ability and relevant project or research experience over years on paper.
- Location-based hybrid policy: Precica is based in Manchester, UK. This role is hybrid — specific in-office expectations will be confirmed during the interview process.
- Visa sponsorship: We do not currently sponsor visas — we don't hold a sponsorship licence. You must already have the right to work in the UK to apply for this role.
We encourage you to apply even if you don't believe you meet every single qualification listed. Not all strong candidates tick every box, and research shows that people from underrepresented groups are especially prone to underestimating their own fit — so please don't rule yourself out prematurely. Problems like the one we're solving benefit from a wide range of perspectives, and we're building Precica with that in mind.
Your safety matters to us. To protect yourself from potential scams, remember that Precica recruiters only contact you from @precica.com email addresses — be cautious of emails from other domains claiming to represent us. Legitimate Precica recruiters will never ask for money, fees, or banking information before your first day. If you're ever unsure about a communication, don't click any links — get in touch with us directly at contact@precica.com to confirm.
How We're Different
Small team, big infrastructure problem.
We believe infrastructure problems with real cost, energy, and carbon footprint attached deserve first-principles engineering, not incremental fixes. At Precica we work as a small, tightly-knit founding team rather than siloed subteams, and we value shipping real capability into live pilots over polishing research for its own sake. We treat compression as a systems problem as much as a modelling one — we're already running pilots in electricity markets while expanding into unstructured data. As one of our first technical hires, you won't be handed a narrow ticket queue — you'll help define how the core product works.
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