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Coinbase | Remote | $98k - $156k | |||
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This job is closed
About the Team:
The AI & Biometrics team is building a state-of-the-art iris recognition engine that works on the 1B+ people scale. In order to do this, we use a fusion of custom optics, hardware, and on-device machine learning, combined with large-scale data collection in more than 20 countries to amass over several million images monthly. These images need to be pre-processed and passed through both external and in-house labelling services.
About the Opportunity:
From field tests all over the world we receive data from various demographics to train our ML models. These images need to be pre-processed and passed through our labelling services before they can be used for training neural networks. This role is responsible for building, scaling, and maintaining a stable data pipeline.
In this role you will:
- Design data pipelines to handle large scale data ingest. This includes figuring out ways to store and process this data with robust features for filtering, pre-processing, and versioning.
- Build out data infrastructure to train large neural networks using self-supervised and contrastive learning.
- Build and refine custom data labeling services that directly influence the quality of our iris recognition engine.
- Work closely with other internal stakeholders to incorporate their data usage needs.
About You:
- Have industry experience as a Data Engineer, Machine Learning Engineer, or Data Scientist, dealing with data infrastructure, distributed systems, and fault tolerant data pipelines.
- Experience deploying models and infrastructure on Kubernetes.
- Experience with infrastructure tools for provisioning, deployment, and monitoring such as Terraform, AWS, Docker, and Datadog.
- Experience with heterogeneous data sources and data models including MongoDB, PostgreSQL, Redis, and Neo4J.
- Own problems end-to-end, and are willing to pick up whatever context is needed.
- You enjoy working as part of a fast-moving team, where perfectionism can sometimes be at odds with pragmatism.
- A desire to dig into problems across the stack, whether networking issues, performance bottlenecks, memory leaks, or simply reading unfamiliar code to figure out where potential issues might exist.
- Have a strong belief in the crucial need of high-quality data for producing state of the art machine learning systems.
#LI-Remote
Is machine learning a good career?
Yes, machine learning is a rapidly growing field and can be a very promising career option for those interested in it
As businesses and industries increasingly rely on data to drive decision-making, there is a growing need for skilled professionals who can analyze and make sense of this data
Machine learning, which involves developing algorithms that can learn from and make predictions on large datasets, is a crucial part of this process
Machine learning careers can range from data analysts, machine learning engineers, data scientists, and more
These professionals work in a variety of industries, including finance, healthcare, e-commerce, and technology
The demand for machine learning experts is high, and the salaries in this field are also generally quite competitive
However, it's important to note that machine learning can be a complex field that requires a strong background in mathematics, statistics, and computer science
It also requires ongoing learning and staying up-to-date with the latest developments and tools in the field
If you enjoy working with data, have a strong interest in programming, and are willing to put in the effort to stay current with developments, a career in machine learning can be very rewarding.