The post holder is a member of the R&D team, reporting to the company’s Technical Director, and leads the development, training, and ongoing improvement of the AI agent that operates our automated experimental platform. The role combines hands‑on machine learning engineering with ownership of the training strategy — defining what data the agent needs, how that data is collected and structured, how the agent is trained and evaluated, and how it is adapted to improve real‑world performance.
The post holder is highly skilled in machine learning engineering, with strong knowledge of agent architectures, training pipelines, and evaluation methodology, and is comfortable working with models that control physical hardware rather than operating on static datasets alone. The person will work closely with our neuroscience, hardware engineers, and software team to translate domain expertise into training objectives, data specifications, and measurable performance criteria.
Responsibility
Agent Development
- Design, build, and maintain the AI agent that plans and executes automated experimental workflows on the platform.
- Select and implement appropriate learning approaches (e.g. imitation learning, reinforcement learning, supervised policy learning, or hybrid rule-and-model architectures) for each stage of the workflow.
- Define the agent’s action space, observation space, decision rules, and success and failure conditions in collaboration with domain experts.
- Develop and maintain training, fine‑tuning, and deployment pipelines, including model versioning and reproducibility.
- Integrate the agent with real‑time control software, sensors, and instrument hardware in partnership with the software and engineering teams.
- Build simulation or offline test environments where these reduce reliance on scarce or costly experimental runs.
Training Data and Strategy
- Specify what data is required to train the agent effectively — including data types, sampling rates, annotation schemes, volume, and coverage of edge cases and failure modes.
- Work with experimental staff to design and implement data collection protocols that produce clean, well‑structured, and reusable training datasets.
- Build and maintain data pipelines for ingestion, labelling, quality control, versioning, and storage.
- Identify gaps, imbalances, and biases in the training data, and recommend targeted collection to address them.
- Continuously assess the return on additional data versus changes to model architecture, training regime, or reward and objective design.
Evaluation and Performance
- Define and maintain evaluation metrics and benchmark test suites that reflect real experimental success, not proxy measures alone.
- Analyse agent performance, diagnose failure modes, and identify their root causes in the model, the data, or the surrounding system.
- Adjust and retrain the agent to improve success rate, consistency, and robustness across different samples, operators, and hardware configurations.
- Monitor deployed agent performance and establish processes for detecting drift and triggering retraining.
- Document model behaviour, limitations, and known failure conditions to a standard suitable for a regulated or quality‑controlled product environment.
General
- Contribute to strategic decisions on AI and software architecture for the current developing system and planned future capabilities.
- Manage and monitor machine learning resources, compute, and budgets in relevant areas.
- Support deployment, troubleshooting, or performance investigation of the agent at customer or partner sites as required.
- Contribute to technical documentation, patents, and reports as needed.
- Train or mentor other team members on machine learning methods, tooling, or data practices where relevant.
Selection criteria
- Degree in Computer Science, Machine Learning, Engineering, Mathematics, Physics, or a closely related subject.
- Demonstrable experience building, training, and deploying machine learning models in a production or applied research setting.
- Strong programming skills in Python and practical proficiency with a modern deep learning framework (e.g. PyTorch, TensorFlow, JAX).
- Experience designing training pipelines end to end, including data preparation, training, evaluation, and iteration.
- Ability to specify data requirements from first principles — determining what data a system needs to learn a task well, and why.
- Experience diagnosing model failure and improving performance through changes to data, architecture, objectives, or training regime.
- Ability to work effectively with domain experts outside their own field, extracting the structure of a problem from people who understand the science but not the machine learning.
- Ability to communicate technical information clearly, both verbally and in writing, to a multidisciplinary team.
- Strong problem‑solving skills, with the ability to identify root causes and implement practical, robust solutions.
- Ability to work independently and take ownership of projects, while collaborating effectively across engineering disciplines.
- Deep understanding of intuitive neuro‑network and solid mathematic background.
- Experience with agentic systems, sequential decision‑making, reinforcement learning, or imitation learning.
- Experience applying machine learning to physical systems, robotics, laboratory automation, or closed‑loop instrument control.
- Experience with computer vision applied to microscopy or other scientific imaging.
- Experience working with small, expensive, or slow‑to‑collect datasets, and techniques for learning efficiently from them.
- Experience with MLOps tooling for experiment tracking, model versioning, and deployment monitoring.
- Experience in a start‑up or scale‑up environment, particularly taking a system from research prototype to a supportable product.
- Experience with biological or life science instrumentation, or willingness to develop working knowledge of the scientific domain.
Salary level: £60,000 - 80,000 annual depending on qualification and experience.
Pre‑employment screening
All offers of employment are made subject to standard pre‑employment screening, as applicable to the post. If you are offered the post, you will be asked to provide proof of your right‑to‑work, your identity, and we will contact the referees you have nominated. You will also be asked to complete a health declaration (so that you can tell us about any health conditions or disabilities so that we can discuss appropriate adjustments with you), and a declaration of any unspent criminal convictions.
About Bioptix Ltd
Bioptix is a neuroscience technology company founded in 2021, developing intelligent automation solutions designed to make advanced brain research more accessible, reproducible, and scalable. Our products automate critical stages of neuroscience experiments, helping researchers overcome the most technically demanding processes in neuroscience.
Bioptix brings together expertise across neuroscience, electrophysiology, robotics, imaging, software engineering, artificial intelligence, and technology commercialisation. As we continue to grow, our long‑term ambition is to help create a more connected understanding of the brain by bringing together neural function, structure, and molecular identity through a broader platform of integrated research technologies.
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