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For Companies At a Glance
- Tasks: Lead and innovate in machine learning projects, from ideation to deployment.
- Company: Join a forward-thinking company focused on cutting-edge technology and innovation.
- Benefits: Enjoy a competitive pay range and a hybrid work schedule for flexibility.
- Why this job: Be at the forefront of machine learning, driving impactful projects and mentoring others.
- Qualifications: Bachelor's degree in relevant fields; 7+ years of machine learning experience required.
- Other info: Master’s degree and certifications in ML libraries are preferred.
The predicted salary is between 48000 - 84000 £ per year.
What you’ll be working on
Research, develop, and implement machine learning algorithms for use in software and hardware applications.
Your day-to-day
- Leads complex model development projects to introduce advanced machine learning techniques and algorithms, ensuring integration with production systems. Lead problem-solving efforts across projects.
- Architects and optimises data infrastructure to support scalable machine learning applications.
- Drives strategic decisions in project and product meetings, ensuring alignment of machine learning goals with business objectives.
- Spearheads initiatives, piloting and integrating new technologies into the business workflow.
- Drives innovation through advanced research projects, leading to patentable technology and publications.
- Mentor team members in machine learning and advanced troubleshooting techniques to ensure that best practices are followed.
- Executes end-to-end machine learning model development from ideation to deployment. Optimises model performance and scalability.
- Builds, deploys, monitors, and continuously optimises ML models and developing automated ML models’ training and inference pipelines.
- Develops training and cross-validation data sets for machine learning algorithms.
- Translates product management, engineering and business constraints and queries into tractable data science questions.
- Designs and maintains robust data pipelines for real-time data processing and analysis.
- Leads the troubleshooting of complex data challenges.
- Develops frameworks and tools to improve model performance and insights.
- Performs other related duties and projects as business needs require at direction of management.
You should apply if
- Bachelor’s degree in Computing Science, Data Science, Machine Learning, Applied Mathematics, Statistics, or related field; or any equivalent education and/or experience from which comparable knowledge, skills and abilities have been demonstrated/achieved. Master’s degree preferred.
- Minimum seven (7) years of experience in Machine Learning.
Even better if you have
- Certification in Machine Learning libraries such as Tensorflow, PyTorch, Scikit-learn, NumPy, and Pandas preferred.
Pay range: Competitive
Hybrid work schedule
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Machine Learning Engineer, Sr. employer: ORB Sport
Contact Detail:
ORB Sport Recruiting Team
StudySmarter Expert Advice 🤫
We think this is how you could land Machine Learning Engineer, Sr.
✨Tip Number 1
Make sure to showcase your experience with complex model development projects. Highlight specific examples where you've led initiatives that introduced advanced machine learning techniques, as this aligns closely with what we're looking for.
✨Tip Number 2
Familiarize yourself with our current technology stack and be prepared to discuss how you can integrate new technologies into our workflow. This shows that you're proactive and ready to drive innovation.
✨Tip Number 3
Emphasize your mentoring experience in machine learning. We value team collaboration and knowledge sharing, so demonstrating your ability to guide others will set you apart.
✨Tip Number 4
Be ready to discuss your approach to optimizing model performance and scalability. Providing insights into your problem-solving strategies will demonstrate your expertise and fit for the role.
We think you need these skills to ace Machine Learning Engineer, Sr.
Some tips for your application 🫡
Highlight Relevant Experience: Make sure to emphasize your experience in machine learning, especially any projects where you led model development or integrated advanced algorithms. Use specific examples that demonstrate your ability to drive innovation and solve complex problems.
Showcase Technical Skills: List your proficiency with machine learning libraries such as TensorFlow, PyTorch, Scikit-learn, NumPy, and Pandas. If you have certifications, be sure to mention them prominently in your application.
Tailor Your Application: Customize your CV and cover letter to align with the job description. Highlight how your background in computing science, data science, or related fields meets the requirements of the position.
Demonstrate Leadership and Mentorship: Include examples of how you've mentored team members or led projects. This will show your capability to guide others in machine learning best practices and contribute to a collaborative work environment.
How to prepare for a job interview at ORB Sport
✨Showcase Your Technical Expertise
Be prepared to discuss your experience with machine learning algorithms and frameworks like TensorFlow, PyTorch, and Scikit-learn. Highlight specific projects where you led model development or optimization, and be ready to explain the technical challenges you faced and how you overcame them.
✨Demonstrate Problem-Solving Skills
Since the role involves leading problem-solving efforts, come equipped with examples of complex data challenges you've tackled. Discuss your approach to troubleshooting and how you ensured alignment between machine learning goals and business objectives.
✨Emphasize Leadership and Mentorship
As a senior position, showcasing your leadership skills is crucial. Talk about your experience mentoring team members in machine learning best practices and how you've driven innovation within your team or organization.
✨Prepare for Strategic Discussions
Expect questions about how you would drive strategic decisions in project meetings. Be ready to discuss how you would integrate new technologies into existing workflows and how you align machine learning initiatives with broader business goals.
