At a Glance
- Tasks: Develop and deploy cutting-edge ML infrastructure for world-class user experiences.
- Company: Join Blockchain.com, a leader in the tech industry with a focus on innovation.
- Benefits: Competitive salary, flexible work options, and opportunities for professional growth.
- Other info: Dynamic team environment with excellent career advancement opportunities.
- Why this job: Make a real impact in the exciting world of machine learning and data science.
- Qualifications: Experience in machine learning pipelines and a passion for tech innovation.
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 in London employer: Blockchain.com
At Blockchain.com, we pride ourselves on being an exceptional employer that fosters a collaborative and innovative work culture. Our commitment to employee growth is evident through continuous learning opportunities and the chance to work on cutting-edge machine learning projects that have a real impact on our products. Located in a vibrant tech hub, we offer competitive benefits and a dynamic environment where your contributions are valued and recognised.
StudySmarter Expert Advice🤫
We think this is how you could land Machine Learning Engineer in London
✨Tip Number 1
Network like a pro! Reach out to folks in the industry, attend meetups, and connect with people on LinkedIn. You never know who might have the inside scoop on job openings or can refer you directly.
✨Tip Number 2
Show off your skills! Create a portfolio showcasing your machine learning projects. Whether it's a GitHub repo or a personal website, having tangible examples of your work can really set you apart from the crowd.
✨Tip Number 3
Prepare for interviews by practising common ML questions and coding challenges. Use platforms like LeetCode or HackerRank to sharpen your skills. The more prepared you are, the more confident you'll feel!
✨Tip Number 4
Don't forget to apply through our website! We love seeing applications come directly from candidates who are excited about joining us. Plus, it shows you're genuinely interested in being part of the team!
We think you need these skills to ace Machine Learning Engineer in London
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!
Showcase Your Projects:Include any projects you've worked on that demonstrate your ability to develop and deploy ML infrastructure. We love seeing real-world applications of your skills, so don’t hold back!
Be Clear and Concise:When writing your application, keep it clear and concise. Use bullet points where possible to make it easy for us to read through your qualifications and experiences quickly.
Apply Through Our Website:Don’t forget to apply through our website! It’s the best way for us to receive your application and ensures you’re considered for the role. We can’t wait to hear from you!
How to prepare for a job interview at Blockchain.com
✨Know Your ML Fundamentals
Make sure you brush up on your machine learning fundamentals. Understand the end-to-end ML pipeline, from data collection to model deployment. Be ready to discuss how you've applied these concepts in real-world scenarios, especially in relation to the responsibilities outlined in the job description.
✨Showcase Your Coding Skills
Since coding is a big part of this role, be prepared to demonstrate your coding skills during the interview. Practice writing clean, efficient code in Python or any other relevant languages. You might even be asked to solve a problem on the spot, so get comfortable with coding challenges!
✨Discuss Your Experience with MLOps
Highlight your experience with MLOps tools and practices. Talk about how you've managed model versioning, logging, and monitoring in previous projects. This will show that you understand the importance of maintaining production-level systems and can contribute to the team's goals effectively.
✨Prepare Questions for Them
Interviews are a two-way street! Prepare insightful questions about their current ML projects, team dynamics, and future goals. This not only shows your interest in the role but also helps you gauge if the company is the right fit for you.
