Senior Machine Learning Scientist - Applied Research
United KingdomFull-TimeSenior
Salary not disclosed
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Job Details
- Languages
- Fluent in written and spoken English.
- Experience
- At least 5 years of industry experience
- Required Skills
- DockerPythonSQLMachine LearningPyTorchNosqlSoftware EngineeringAWS LambdaDeep Learningscikit-learnPrompt Engineering
Requirements
- Master's degree or PhD in Computer Science, Electrical Engineering, AI, Machine Learning, applied math or related field or outstanding previous achievements demonstrating excellence in Deep Machine Learning, Computer Science and Software Engineering.
- At least 5 years of industry experience in Machine / Deep Learning, Computer Science and Software Engineering.
- A strong understanding of the math and theory behind machine learning and deep learning is a prerequisite.
- Academic publications in peer reviewed conferences or journals related to Machine Learning - preferably A/A+ rated.
- Machine / Deep Learning development skills, including popular platforms (AWS SageMaker, Hugging Face, Transformers, PyTorch, PyTorch Lightning, Ray, scikit-learn, Jupyter, Weights & Biases etc.).
- An understanding of Language Models, using and training / fine-tuning and a familiarity with industry-standard LM families.
- Excellent communication and teamwork skills.
- Fluent in written and spoken English.
- Experience working with text data to build Deep Learning and ML models, both supervised and unsupervised.
- A Computer Science educational background is preferred as opposed to statistics or pure mathematics.
- Familiarity in building front-ends (Gradio, Streamlit, Dash or more standard React, Javascript, Flask) for simple demos, POCs and prototypes.
- Experience with advanced prompting / agentic-systems and fine-tuning or training an LLM, using industry accepted platforms.
- Familiarity in coding for at-scale production, ranging from best practices to building back-end API services or stand-alone libraries.
- Essential dev-ops skills (Docker, AWS EC2/Batch/Lambda).
Responsibilities
- Research and develop production grade Machine Learning models.
- Optimize models for scaled production usage.
- Work with colleagues to explore ongoing product issues, challenges and opportunities and then recommend innovative ML/AI based solutions.
- Help out with ad-hoc one-off tasks as a team player within the AI team.
- Work with subject matter experts to curate and generate optimal datasets following responsible data collection and model maintenance practices.
- Explore and access SQL, no-SQL and web data and write efficient parallel pipelines. Review and design datasets to ensure data quality.
- Investigate weaknesses of models in production and work on pragmatic solutions.
- Utilize, adopt, and fine-tune off the shelf models, including LLMs exposed via API and locally hosting LMs and other foundation models.
- Stay current in the field - read research papers, experiment with new architectures and LLMs, and share your findings.
- Write clean, efficient, and modular code with automated tests and appropriate documentation.
- Work with downstream teams to productionize your work and ensure that it makes into a product release.
- Communicate insights, as well as the behavior and limitations of models, to peers, subject matter experts, and product owners.
- Present and publish your work.
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