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- Tasks: Develop and implement cutting-edge machine learning algorithms and collaborate with experts.
- Company: Join Jackson Hogg, a leader in software development and data solutions.
- Benefits: Enjoy a full-time role with opportunities for remote work 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 collaboration.
The predicted salary is between 42000 - 84000 £ 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
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. Being able to discuss recent advancements or projects you've worked on using these tools can really impress during interviews.
✨Tip Number 2
Engage with the machine learning community by attending meetups, webinars, or conferences. Networking with professionals in the field can provide valuable insights and potentially lead to referrals for positions like the one at StudySmarter.
✨Tip Number 3
Prepare to demonstrate your coding skills in Python during technical interviews. Practising coding challenges on platforms like LeetCode or HackerRank can help you sharpen your abilities and boost your confidence.
✨Tip Number 4
Showcase your ability to work collaboratively by discussing past experiences where you successfully collaborated with scientists or developers. Highlighting your teamwork skills can set you apart as a candidate who fits well within our collaborative environment.
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 roles where you've developed algorithms or worked with large datasets.
Craft a Strong Cover Letter: In your cover letter, express your passion for machine learning and how your skills align with the responsibilities outlined in the job description. Mention any relevant collaborations with scientists or experience in cloud deployment.
Showcase Relevant Projects: If you have worked on any notable projects involving machine learning, be sure to include them in your application. Describe your role, the technologies used, and the outcomes achieved to demonstrate your capabilities.
Prepare for Technical Questions: Anticipate technical questions related to machine learning algorithms and coding practices. Brush up on your knowledge of SQL, Julia, and version control systems like git, as these may come up during the interview process.
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 Python, PyTorch, or TensorFlow, and be ready to explain your thought process and the outcomes.
✨Demonstrate Collaboration Experience
Since the role involves working closely with scientists and other developers, share examples of how you've successfully collaborated in a team setting. Discuss any experiences where you exchanged ideas based on evidence and how that contributed to project success.
✨Prepare for Problem-Solving Questions
Expect to face technical challenges during the interview. Brush up on your problem-solving skills and be ready to tackle hypothetical scenarios related to data analysis and algorithm development. Think aloud as you work through these problems to showcase your critical mindset.
✨Communicate Clearly and Confidently
You'll need to present your work to both technical and non-technical audiences. Practice explaining complex concepts in simple terms and be prepared to discuss your projects and findings clearly. This will demonstrate your ability to create and maintain relationships with external collaborators.
