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- Tasks: Design and run experiments to enhance large-scale generative models.
- Company: Join a cutting-edge tech company focused on innovative machine learning solutions.
- Benefits: Enjoy flexible working options and access to the latest tech tools.
- Other info: Background in generative models or 3D data is a bonus!
- Why this job: Be at the forefront of ML research, making a real impact in the field.
- Qualifications: Strong Python skills and experience with ML frameworks like PyTorch or JAX required.
The predicted salary is between 36000 - 60000 £ per year.
Overview:
We\’re looking for a research-focused ML engineer to design and run experiments that improve large-scale generative models. You\’ll explore new ideas from recent research, run ablation studies, and help integrate insights into production-ready training pipelines.
What You\’ll Do:
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Design and run experiments to evaluate and improve model performance
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Implement ideas from recent research papers using large-scale datasets
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Collaborate with engineering teams to refine training workflows
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Contribute to the development of robust baselines and scalable pipelines
What We\’re Looking For:
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Strong Python skills and experience with ML frameworks (e.g., PyTorch, JAX)
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Familiarity with model fine-tuning and training evaluation at scale
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Ability to translate research into practical implementations
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Background in generative models and/or 3D data is a plus
Research Engineer - ML employer: Harnham
Contact Detail:
Harnham Recruiting Team
StudySmarter Expert Advice 🤫
We think this is how you could land Research Engineer - ML
✨Tip Number 1
Familiarise yourself with the latest research in generative models. This will not only help you understand the current landscape but also allow you to discuss recent advancements during interviews, showcasing your passion and knowledge in the field.
✨Tip Number 2
Engage with the ML community by attending webinars, workshops, or conferences. Networking with professionals in the field can provide valuable insights and potentially lead to referrals, which can significantly boost your chances of landing the job.
✨Tip Number 3
Work on personal projects that involve implementing ideas from research papers. This hands-on experience will not only enhance your skills but also give you concrete examples to discuss during interviews, demonstrating your ability to translate theory into practice.
✨Tip Number 4
Collaborate on open-source projects related to ML frameworks like PyTorch or JAX. This will not only improve your coding skills but also show potential employers your commitment to continuous learning and collaboration, which are key traits for a Research Engineer role.
We think you need these skills to ace Research Engineer - ML
Some tips for your application 🫡
Tailor Your CV: Make sure your CV highlights your strong Python skills and experience with ML frameworks like PyTorch or JAX. Include specific projects or experiences that demonstrate your ability to design and run experiments, as well as any familiarity with generative models.
Craft a Compelling Cover Letter: In your cover letter, express your enthusiasm for the role and the company. Discuss how your background aligns with their needs, particularly your experience in model fine-tuning and training evaluation at scale. Mention any relevant research papers you've implemented ideas from.
Showcase Relevant Projects: If you have worked on projects involving large-scale datasets or generative models, be sure to include these in your application. Describe your role, the challenges you faced, and the outcomes of your work to demonstrate your practical implementation skills.
Proofread and Edit: Before submitting your application, take the time to proofread and edit your documents. Ensure there are no grammatical errors and that your writing is clear and concise. A polished application reflects your attention to detail and professionalism.
How to prepare for a job interview at Harnham
✨Showcase Your Python Proficiency
Make sure to highlight your strong Python skills during the interview. Be prepared to discuss specific projects where you've used Python, especially in relation to ML frameworks like PyTorch or JAX.
✨Discuss Recent Research
Familiarise yourself with recent research papers relevant to generative models. Be ready to discuss how you can implement ideas from these papers into practical applications, demonstrating your ability to bridge theory and practice.
✨Prepare for Technical Questions
Expect technical questions related to model fine-tuning and training evaluation at scale. Brush up on your knowledge of these topics and be prepared to explain your thought process clearly.
✨Emphasise Collaboration Skills
Since the role involves collaborating with engineering teams, be sure to share examples of past teamwork experiences. Highlight how you contributed to refining workflows and integrating insights into production-ready pipelines.