SWE v/s MLE
See how the roles differ from a Senior SWE and a Senior MLE at Meta
This newsletter is a guest post with
who is a Senior Machine Learning Engineer at Meta and in this, we will be comparing how SWEs and ML operate
Machine Learning Engineers (MLEs) and Software Engineers (SWEs) are two key roles at almost every tech company, working closely to deliver crucial company objectives, especially when a product requires cutting-edge machine learning technology. While these roles often collaborate, they have distinct responsibilities and mindsets, ultimately working toward a common goal: delivering innovative products efficiently.
Defining the Roles
Machine Learning Engineers (MLEs):
MLEs focus on machine learning modeling and data analysis. Depending on the company, their titles may vary (e.g., Applied Scientist, AI Engineer, or Data Scientist). Their work often involves model development, data processing, and driving critical business metrics through AI/ML applications.
Software Engineers (SWEs):
SWEs are responsible for building products, designing and developing infrastructure, including ML infrastructure in some cases. They focus on building products and creating robust, scalable software systems that are foundational for the company's products and services.
Key Differences
Let’s explore these roles across three primary areas
Responsibilities
Development Process
Skills Requirement
Career Opportunities.
1. Responsibilities
Both SWEs and MLEs are problem solvers at their core, but the nature of the problems they solve differs significantly.
Problem Solving
SWE:
Focus: SWEs seek the most efficient way to implement features or build scalable systems. They focus on the question: "How can we build this feature in the best way?"
Approach: Their work is deterministic, meaning given a set of conditions, their deliverables should behave predictably. Their primary focus is on code quality, performance, and scalability.
MLE:
Focus: MLEs often tackle ambiguous problems like, "Why isn't this feature performing as expected?" Their work revolves around hypothesis testing and model optimization.
Approach: MLEs deal with non-deterministic deliverables that require experimentation. They refine models through iteration and data analysis, aiming to improve business outcomes.
Expectations
SWE:
Scope: Owns team infrastructure and leads product development.
Complexity: Works within defined problem spaces, focusing on system design and scalability.
Execution: Expected to write production-ready code, ensure testing coverage, and maintain robust systems.
Soft Skills: Ability to communicate ideas through effective system design proposals, understand and communicate design tradeoffs, building alignment with cross functioning teams.
MLE:
Scope: Focuses on developing and optimizing ML models, often owning end-to-end ML pipelines.
Complexity: Operates in ambiguous problem spaces where goals are less defined, requiring innovation and alignment with business goals.
Execution: Stays current with research, iterates on models, and continuously tests new hypotheses.
Soft Skills: conduct scientific understanding of a problem and breakdown complex problems into actionable insights, communicate critical metrics to business stakeholders,
Transitioning from SWE to MLE required a mindset shift. I went from designing systems with predictable outcomes to experimenting with models that often had uncertain results. Focusing on business objectives rather than technology helped me adjust to this new approach.
– Kartik, Senior MLE at Meta (Check out his full story here)
2. Development Process
The development processes and tools used by SWEs and MLEs reflect their differing focus areas.
SWE Development:
Designs scalable infrastructure and writes production-ready code.
Relies heavily on unit and integration testing within CI/CD pipelines.
Continuous development and updates are key to their workflow.
MLE Development:
Builds and experiments with machine learning models.
Focuses on prototyping and testing models before deployment.
Iterative development is the norm, especially when working on A/B tests for model performance.
Tip: The line between SWE and MLE development is blurring. Tools like MLFlow and AWS SageMaker have introduced CI/CD practices into ML development, streamlining the model training and deployment process.
3. Skills Requirement
Both roles share many transferable skills, particularly in documentation, communication, and collaboration. However, they diverge in technical expertise.
SWE Skills:
Languages: Java, C++, Python (any programming language) if you are backend focussed. React, Angular etc if you are frontend focussed.
Tools: Distributed systems (e.g., Spark), SQL/NoSQL databases, cloud platforms (AWS, Azure, GCP).
Broad Knowledge: Understanding of microservices, cloud computing, and system design.
MLE Skills:
Languages: Python (primarily), R, SQL.
Tools: ML frameworks (PyTorch, TensorFlow), data analysis libraries (Pandas, NumPy), deployment platforms (SageMaker, MLFlow).
Specialized Expertise: MLEs often have deeper knowledge of domains like NLP, Computer Vision, or Robotics.
For a deeper dive into MLE tools and resources, check out this article: Top Resources to Get Started in ML
4. Career Opportunities
Career progression differs between SWEs and MLEs due to the nature of their work.
SWE Career Progression:
Broader opportunities due to the general applicability of their skills across various domains.
SWEs often progress quickly since their skills are needed in numerous industries and projects.
MLE Career Progression:
Slower advancement due to the niche expertise, but often compensated at higher levels, especially when contributing to business-critical AI projects.
Growth opportunities for MLEs can be faster in organizations prioritizing AI, especially in research-heavy roles.
As you rise through the ranks (Principal/Staff+), the gap between SWEs and MLEs in terms of pay and opportunities narrows, as both roles require deep expertise in their domains.
Final Thoughts
Both positions offer unique challenges and exciting opportunities, especially as the landscape of AI and software development continues to evolve.
Ultimately, your choice depends on where your interests lie. Are you passionate about building robust, scalable systems, or are you intrigued by the potential of machine learning to solve complex, ambiguous problems? Either way, both paths offer substantial growth and impact.
While the responsibilities of MLEs and SWEs are distinct, they ultimately work towards a shared goal: delivering new products and features as efficiently and effectively as possible
As always, Happy Learning!
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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.
Great read !
Thanks for sharing this insight