At a Glance
- Tasks: Develop and deploy cutting-edge ML infrastructure for world-class user experiences.
- Company: Join Blockchain.com, a leader in the crypto space with a dynamic team.
- Benefits: Competitive salary, flexible work options, and opportunities for professional growth.
- Other info: Collaborative environment with excellent career advancement opportunities.
- Why this job: Make a real impact in the fast-paced world of machine learning and blockchain technology.
- Qualifications: Experience in machine learning pipelines and a passion for innovative tech.
The predicted salary is between 60000 - 80000 £ per year.
About the Role
Blockchain.com is seeking a Machine Learning Engineer to join our Data Science and Business Intelligence team. Data exploitation is central to our business, and in this role, you will play a crucial part in developing and deploying ML Infrastructure to enable world-class user experiences across all our products. You will support the organization in various areas including experimentation, fraud detection, market signals, marketing, and pricing.
Responsibilities
- Develop and deploy ML Infrastructure, including feature store, data and model version control, training pipelines, inference serving, logging, and scaling systems.
- Consistently advance the state of ML for your problem domain, setting and executing against roadmaps.
- Define projects for other engineers.
- Own the full ML life cycle for significant new ML products, including production quality and continuous improvements.
- Complement data scientists by contributing to a reliable, secure, and maintainable modeling framework for production model deployment.
- Advocate for ML excellence.
- Code deliverables in tandem with Data Scientists.
Requirements
- Experience with developing end-to-end machine learning pipelines that ensure consistency between development and production environments.
- Ability to design ML architectures for scale with site traffic and complexity of features for predictive algorithms.
- Care with regards to model and data versioning, resource allocation and scaling, and logging to build optimal systems.
- Experience with creating systems that monitor and react to faults in resources, data streams and model responses.
Nice to Have
- Experience with Airflow or Google Composer.
- Experience with Python and other programming languages such as Java, Kotlin or Scala.
- Experience with Spark or other Big Data frameworks.
- Experience with Kubernetes for data and ML workloads.
- Experience working with open-source machine learning libraries.
- Experience with commonly used ML Libraries: Xgboost, lgbm, sklearn.
Machine Learning Engineer employer: Blockchain.com
At Blockchain.com, we pride ourselves on being an exceptional employer that fosters a collaborative and innovative work culture. As a Machine Learning Engineer, you will have the opportunity to work with cutting-edge technology in a dynamic environment, while benefiting from continuous learning and growth opportunities. Our commitment to employee development, coupled with our focus on creating world-class user experiences, makes Blockchain.com a rewarding place to advance your career.
StudySmarter Expert Advice🤫
We think this is how you could land Machine Learning Engineer
✨Tip Number 1
Network like a pro! Reach out to folks in the industry on LinkedIn or at meetups. A friendly chat can open doors that a CV just can't.
✨Tip Number 2
Show off your skills! Create a portfolio showcasing your machine learning projects. This is your chance to shine and demonstrate what you can bring to the table.
✨Tip Number 3
Prepare for interviews by practising common ML questions and coding challenges. We all know that confidence is key, so get comfortable with your knowledge!
✨Tip Number 4
Don't forget to apply through our website! It’s the best way to ensure your application gets seen by the right people. Plus, we love seeing candidates who are keen to join us!
We think you need these skills to ace Machine Learning Engineer
Some tips for your application 🫡
Tailor Your CV:Make sure your CV is tailored to the Machine Learning Engineer role. Highlight relevant experience, especially with ML pipelines and architectures. We want to see how your skills align with our needs!
Craft a Compelling Cover Letter:Your cover letter is your chance to shine! Share your passion for machine learning and how you can contribute to our team. Be sure to mention any specific projects or experiences that relate to the job description.
Showcase Your Projects:If you've worked on any cool ML projects, don’t hold back! Include links to your GitHub or any other platforms where we can see your work. We love seeing practical applications of your skills!
Apply Through Our Website:We encourage you to apply directly through our website. It’s the best way to ensure your application gets into the right hands. Plus, it shows us you're serious about joining our team!
How to prepare for a job interview at Blockchain.com
✨Know Your ML Fundamentals
Brush up on your machine learning basics, especially around end-to-end pipelines and model deployment. Be ready to discuss how you've designed ML architectures for scale and any experience you have with MLOps tools. This will show that you understand the core responsibilities of the role.
✨Showcase Your Problem-Solving Skills
Prepare to share specific examples of technical challenges you've faced in previous projects. Highlight how you approached these problems, particularly in developing reliable and maintainable systems. This will demonstrate your ability to tackle complex issues that are crucial for the role.
✨Familiarise Yourself with Their Tech Stack
Research the technologies mentioned in the job description, like Python, Spark, or Kubernetes. If you have experience with tools like Airflow or Google Composer, be sure to mention it. Showing familiarity with their tech stack can give you an edge and make you a more appealing candidate.
✨Prepare Questions About Their Projects
Think of insightful questions related to their current ML projects or future initiatives. This not only shows your interest in the role but also your proactive mindset. Asking about their approach to experimentation or fraud detection can lead to a great discussion and demonstrate your enthusiasm for contributing to their team.
