Comparison of Software Engineers (SWEs) and Machine Learning Engineers (MLEs)
1. Key Differences:
- SWEs focus on the most efficient way to implement features or build scalable systems, while MLEs often tackle ambiguous problems like hypothesis testing and model optimization.
- SWEs work within defined problem spaces and focus on system design and scalability, while MLEs operate in ambiguous problem spaces and continuously test new hypotheses.
2. Responsibilities:
- SWEs own team infrastructure and lead product development, while MLEs focus on developing and optimizing ML models, often owning end-to-end ML pipelines.
- SWEs are expected to write production-ready code and maintain robust systems, while MLEs refine models through iteration and data analysis to improve business outcomes.
3. Development Process:
- SWEs design scalable infrastructure, write production-ready code, and rely on unit and integration testing within CI/CD pipelines.
- MLEs build and experiment with machine learning models, focusing on prototyping, testing, and iterative development.
4. Skills Requirement:
- SWEs have a broad knowledge of microservices, cloud computing, and system design, using languages like Java, C++, Python, React, and Angular.
- MLEs have specialized expertise in ML frameworks, data analysis libraries, deployment platforms, and deeper knowledge of domains like NLP, Computer Vision, or Robotics.
5. Career Opportunities:
- SWEs have broader opportunities due to the general applicability of their skills across various domains and often progress quickly in their careers.
- MLEs experience slower advancement due to niche expertise, but compensation increases, especially when contributing to business-critical AI projects.
6. Final Thoughts:
- Both SWEs and MLEs work toward a shared goal of delivering new products and features efficiently and effectively, offering substantial growth and impact in their respective domains.
- The choice between SWE and MLE ultimately depends on individual interests and passion for building robust, scalable systems or leveraging machine learning to solve complex problems.
Interesting post Hemant and Kartik! When you mention an ML engineer working on a problem where 'a feature is not behaving as expected,' can you give an example of such a feature and how you solve this by building, training, and optimizing a model? Thanks!
@Akos Thats a great question. Let me pick an example.
Feature not behaving as expected: Lets assume we are working on Video recommendations team at Neflix and notice that users outside US has significantly low engagement compared users in US.
Problem Identification and definition: After some deep dive, you found that users who speak a non-english language have significantly low engagement then users with primary language as English for the home recommendations recommendations, irrespective of the country.
After further data analysis, several potential problems might emerge
i) Language as a feature has not been considered as a feature while making recommendations, so model cannot distinguish between an english speaking user and non-english speaking user.
ii) Movies are not labeled correctly, i.e., model cannot distinguish whether a movie is a spanish movie or an english movie
iii) Training dataset used for the model is imbalanced and mostly contains data from english.
iv) Model used is not capable to learn these characteristics.
Potential Solutions:
For each of these issues, you would need to diagnose and address them strategically. For example, you could add language as an explicit feature in the recommendation model, collect more diverse data to balance the training set, or explore new model architectures better suited for multilingual recommendations. This process might also involve updating metadata for existing content or adding new features to ensure the recommendations are as relevant.
Summary from Merlin AI:
Comparison of Software Engineers (SWEs) and Machine Learning Engineers (MLEs)
1. Key Differences:
- SWEs focus on the most efficient way to implement features or build scalable systems, while MLEs often tackle ambiguous problems like hypothesis testing and model optimization.
- SWEs work within defined problem spaces and focus on system design and scalability, while MLEs operate in ambiguous problem spaces and continuously test new hypotheses.
2. Responsibilities:
- SWEs own team infrastructure and lead product development, while MLEs focus on developing and optimizing ML models, often owning end-to-end ML pipelines.
- SWEs are expected to write production-ready code and maintain robust systems, while MLEs refine models through iteration and data analysis to improve business outcomes.
3. Development Process:
- SWEs design scalable infrastructure, write production-ready code, and rely on unit and integration testing within CI/CD pipelines.
- MLEs build and experiment with machine learning models, focusing on prototyping, testing, and iterative development.
4. Skills Requirement:
- SWEs have a broad knowledge of microservices, cloud computing, and system design, using languages like Java, C++, Python, React, and Angular.
- MLEs have specialized expertise in ML frameworks, data analysis libraries, deployment platforms, and deeper knowledge of domains like NLP, Computer Vision, or Robotics.
5. Career Opportunities:
- SWEs have broader opportunities due to the general applicability of their skills across various domains and often progress quickly in their careers.
- MLEs experience slower advancement due to niche expertise, but compensation increases, especially when contributing to business-critical AI projects.
6. Final Thoughts:
- Both SWEs and MLEs work toward a shared goal of delivering new products and features efficiently and effectively, offering substantial growth and impact in their respective domains.
- The choice between SWE and MLE ultimately depends on individual interests and passion for building robust, scalable systems or leveraging machine learning to solve complex problems.
Thanks Henry for this summary. There is also a summary table in the final thoughts section.
Great read !
Thanks for sharing this insight
Interesting post Hemant and Kartik! When you mention an ML engineer working on a problem where 'a feature is not behaving as expected,' can you give an example of such a feature and how you solve this by building, training, and optimizing a model? Thanks!
@Akos Thats a great question. Let me pick an example.
Feature not behaving as expected: Lets assume we are working on Video recommendations team at Neflix and notice that users outside US has significantly low engagement compared users in US.
Problem Identification and definition: After some deep dive, you found that users who speak a non-english language have significantly low engagement then users with primary language as English for the home recommendations recommendations, irrespective of the country.
After further data analysis, several potential problems might emerge
i) Language as a feature has not been considered as a feature while making recommendations, so model cannot distinguish between an english speaking user and non-english speaking user.
ii) Movies are not labeled correctly, i.e., model cannot distinguish whether a movie is a spanish movie or an english movie
iii) Training dataset used for the model is imbalanced and mostly contains data from english.
iv) Model used is not capable to learn these characteristics.
Potential Solutions:
For each of these issues, you would need to diagnose and address them strategically. For example, you could add language as an explicit feature in the recommendation model, collect more diverse data to balance the training set, or explore new model architectures better suited for multilingual recommendations. This process might also involve updating metadata for existing content or adding new features to ensure the recommendations are as relevant.
Great example and perfectly answers my question, thanks Kartik!