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- Tasks: Develop and implement cutting-edge machine learning algorithms and analyse diverse data sources.
- Company: Join Jackson Hogg, a leader in software development and data solutions.
- Benefits: Enjoy a full-time role with opportunities for collaboration and professional growth.
- Why this job: Be part of an innovative team tackling real-world challenges with AI technology.
- Qualifications: Strong coding skills in Python, experience with ML frameworks like PyTorch or TensorFlow required.
- Other info: Mid-senior level position ideal for those passionate about data and machine learning.
The predicted salary is between 36000 - 60000 £ per year.
The Machine Learning Engineer will investigate, implement, and develop novel algorithms using machine learning techniques. They will analyze data from various sources, both internal and external, create pipelines and algorithms to simulate and augment both real and synthetic data, and develop custom ML algorithms to address detection challenges. The role involves using a variety of ML techniques, including deep neural networks. The engineer will deepen their understanding of the scientific principles behind the data by collaborating with relevant subject matter experts such as bioinformaticians and detector physicists when necessary. They will be responsible for writing high-quality software, taking ownership of specific areas of the codebase, and collaborating closely with other developers and scientists.
Qualifications & Skills:
- A good understanding of modern machine learning techniques
- A good foundation of coding practices, preferably in Python
- A reasonable proficiency with Python
- Proficiency with PyTorch or TensorFlow
- A critical mindset and understanding of scientific principles
- Ability to work in a team of scientists and exchange views and ideas based on evidence
- A quality degree in a scientific or software discipline
- Able to work with, process and manage large amounts of data
- High proficiency with Python
- Knowledge of SQL
- Proficiency with Julia
- Familiarity with Linux
- Experience of cloud deployment of software, e.g. AWS
- Knowledge of a Python API package such as FastAPI
- Experience developing software as part of a team
- Familiarity with version control systems, especially git
- High proficiency with PyTorch and TensorFlow
- Ability to confidently produce and deliver presentations on their work, within a team and to a wider audience
- Ability to create and maintain relationships with external collaborators
Seniority level: Mid-Senior level
Employment type: Full-time
Job function: Information Technology, Software Development
Machine Learning Engineer employer: Jackson Hogg
Contact Detail:
Jackson Hogg Recruiting Team
StudySmarter Expert Advice 🤫
We think this is how you could land Machine Learning Engineer
✨Tip Number 1
Familiarise yourself with the latest machine learning techniques and frameworks, especially PyTorch and TensorFlow. Engaging in online courses or workshops can help you stay updated and demonstrate your commitment to continuous learning.
✨Tip Number 2
Build a portfolio showcasing your projects that involve machine learning algorithms and data processing. Highlight any experience with cloud deployment, as this is a key aspect of the role and will set you apart from other candidates.
✨Tip Number 3
Network with professionals in the field by attending relevant meetups or conferences. This can provide you with insights into the industry and may even lead to referrals or recommendations for the position.
✨Tip Number 4
Prepare to discuss your collaborative experiences in teams, particularly how you've worked with scientists or other developers. Being able to articulate your teamwork skills will be crucial during interviews, as collaboration is a significant part of the role.
We think you need these skills to ace Machine Learning Engineer
Some tips for your application 🫡
Tailor Your CV: Make sure your CV highlights your experience with machine learning techniques, particularly in Python, PyTorch, and TensorFlow. Include specific projects or achievements that demonstrate your coding practices and understanding of scientific principles.
Craft a Strong Cover Letter: In your cover letter, express your enthusiasm for the role and how your skills align with the responsibilities outlined in the job description. Mention your ability to collaborate with scientists and your experience in developing custom ML algorithms.
Showcase Relevant Projects: If you have worked on relevant projects, either professionally or as part of your studies, be sure to include these in your application. Highlight any experience with cloud deployment, data management, and version control systems like git.
Prepare for Technical Questions: Anticipate technical questions related to machine learning algorithms and coding practices during the interview process. Brush up on your knowledge of deep neural networks and be ready to discuss your approach to problem-solving in a team environment.
How to prepare for a job interview at Jackson Hogg
✨Showcase Your Technical Skills
Be prepared to discuss your experience with machine learning techniques, particularly deep neural networks. Highlight specific projects where you've implemented algorithms using PyTorch or TensorFlow, and be ready to explain your coding practices in Python.
✨Demonstrate Your Problem-Solving Ability
Expect questions that assess your critical thinking and problem-solving skills. Prepare examples of how you've tackled detection challenges using custom ML algorithms, and be ready to discuss the scientific principles behind your decisions.
✨Emphasise Team Collaboration
Since the role involves working closely with scientists and other developers, be sure to highlight your teamwork experiences. Share examples of how you've successfully collaborated on projects, exchanged ideas, and built relationships with external collaborators.
✨Prepare for Technical Presentations
You may need to present your work during the interview. Practice explaining complex concepts in a clear and engaging manner, as well as how you would communicate your findings to both technical and non-technical audiences.
