We are looking for a Machine Learning Engineer to join
our team in developing and deploying AI-driven
products, including our upcoming AI-powered
sales advisor.
A Machine Learning Engineer will help develop/deploy AI-powered products as we embark on a journey to build a new AI-powered sales advisor.
This is a mid level role, requiring at least 2-3 years of experience in relevant roles, and an academic background in computing or STEM subjects to at least undergraduate level.
Aside from technical expertise, key skills will include communication with stakeholders, ability to understand business requirements and the type of attention to detail that ensures thoroughness in product deployments.
Responsibilities and expertise will be in the field of ML and AI model deployment, monitoring, integration across the tech stack and making best use of data-focussed compute resources.
Diversity is incredibly important to us. Research shows how people from marginalised groups are less likely to apply for a job unless they meet every requirement. However, these accountabilities are a guide and, if you feel like this role could be for you and you don’t meet every criteria, please do apply. We’d love to hear from you.
– Development of a Conversational Recommendation System which utilises Large Language Models (LLMs) and other NLP technologies
– Deploying scalable and cost effective machine learning systems
– Evolving and maintaining our machine learning stack
– Monitoring and analysing deployed models for performance and costs
– Making effective use of cloud infrastructure in our ML stack
– Familiar with the Python-based data science tech stack:
– Python
– Docker & Kubernetes
– AWS Cloud
– Deep learning frameworks – Pytorch and Tensorflow
– HuggingFace ecosystem [optional]
– Other machine learning frameworks – scikit-learn, XGboost, CatBoost etc –
– Ability to understand and develop state-of-the-art implementations
– Familiarity with state-of-the-art deep learning (e.g. transformers) and reinforcement learning (e.g. RLHF), as well as optimised training procedures (e.g. QLora & Adapters)
– Comfortable with the machine learning lifecycle from research to deployment. This includes all things MLOps – model development, validation, deployment and monitoring
– Familiarity with state of the art NLP – text embeddings (representation learning), vector databases, large language models, machine translation, intent recognition, entity recognition, text classification etc
– Familiarity with recommendation engines – collaborative filtering, content-based, hybrids, graph & knowledge based etc [optional]
– Familiarity with CUDA and other hardware (GPU) acceleration for deep learning models [optional]
– Employee Assistance Programme (confidential counselling)
– Medicash healthcare scheme (reclaim costs for dental, physiotherapy, osteopathy and optical care)
– easitBrighton travel scheme (discounted public transport options)
– Cycle to work scheme
– Life Insurance scheme
– 25 days annual leave + bank holidays + your birthday off (rising to 28 after 3 consecutive years with the business & 30 after 5 years)
– Contributory pension scheme
