Goto

Collaborating Authors

Machine Learning Scientist (Distributed Systems, Tensorflow) - Cambridge - November-04-2017 (FcARx)

@machinelearnbot

We are currently seeking a hands-on Machine Learning Scientist (Distributed Systems, Tensorflow) for our new research-led startup, focussing on the application of artificial intelligence in the real world; particularly smart city simulations and bots. We're looking for a hardcore Machine Learning Scientist/Engineer who thrives wants to work with the latest technology in multi-agent learning algorithms, Gaussian process and reinforcement learning. As a Machine Learning Scientist/Engineer, you will be a core member of the machine learning team; working closely with the Machine Learning researchers, transforming their algorithmic research into highly innovative products which will be attractive and accessible to the world. Key Skills: Machine Learning Engineer/ML Scientist, Tensorflow, C, C, Java, Python, C#, Distributed Algorithms. Distributed systems, BSc, MSc, MPhil, PhD, Post-Doc, Research, R&D, startup, Multithreading.


Building a Better Machine Learning Team

#artificialintelligence

The team who gets the business through the prototype and proof of concept phases, is not the same as the team who will monetize machine learning. This key concept is a starting point for moving forward in the machine learning maturity model. So, what does "better" mean? The business comes into machine learning expecting the technology to grow their bottom line. A better team creates the processes, relationships, and infrastructure to meet the need. Those are a combination of technical and soft skills.


The Difference Between Data Scientists and ML Engineers - ALT 4

#artificialintelligence

Although they certainly work together amicably and enjoy some overlap concerning expertise and experience, the two roles serve quite different purposes. Essentially, we are differentiating between Scientists who seek to understand the science behind their work, and Engineers who seek to build something that can be accessed by others. Both roles are extremely important, and at some companies, are interchangeable -- for example, Data Scientists at certain organizations may carry out the work of a Machine Learning engineer and vice versa. To make the distinction clear, I'll split the differences into 3 categories; 1) Responsibilities 2) Expertise 3) Salary Expectations. Data Scientists follow the Data Science Process, which may also be referred to as Blitzstein & Pfister workflow.


The Difference Between Data Scientists and ML Engineers - KDnuggets

#artificialintelligence

Although they certainly work together amicably and enjoy some overlap concerning expertise and experience, the two roles serve quite different purposes. Essentially, we are differentiating between Scientists who seek to understand the science behind their work, and Engineers who seek to build something that can be accessed by others. Both roles are extremely important, and at some companies, are interchangeable -- for example, Data Scientists at certain organizations may carry out the work of a Machine Learning engineer and vice versa. To make the distinction clear, I'll split the differences into 3 categories; 1) Responsibilities 2) Expertise 3) Salary Expectations. Data Scientists follow the Data Science Process, which may also be referred to as Blitzstein & Pfister workflow.


The Difference Between Data Scientists and ML Engineers

#artificialintelligence

Although they certainly work together amicably and enjoy some overlap concerning expertise and experience, the two roles serve quite different purposes. Essentially, we are differentiating between Scientists who seek to understand the science behind their work, and Engineers who seek to build something that can be accessed by others. Both roles are extremely important, and at some companies, are interchangeable -- for example, Data Scientists at certain organizations may carry out the work of a Machine Learning engineer and vice versa. To make the distinction clear, I'll split the differences into 3 categories; 1) Responsibilities 2) Expertise 3) Salary Expectations. Data Scientists follow the Data Science Process, which may also be referred to as Blitzstein & Pfister workflow.