ML Production: Other Technical Roles You Need to Know

As Machine Learning (ML) continues to revolutionize the tech industry, more companies are exploring ways to integrate ML into their products and services. As a result, there is a high demand for ML professionals, including data scientists, ML engineers, and ML operations specialists. However, there are also other technical roles that are vital in ML production, which don’t necessarily require ML knowledge. In this article, we’ll explore some of these roles and what they entail.

ML Infrastructure Engineer and ML Platform Engineer

ML is resource-intensive and relies heavily on infrastructure that scales. Therefore, companies with mature ML pipelines often have infrastructure teams to help them build out the infrastructure for ML. As an ML infrastructure engineer or ML platform engineer, you’ll be responsible for designing, implementing, and maintaining the infrastructure that supports ML projects. This includes setting up servers, networking, storage, and ensuring the infrastructure is optimized for performance and scalability.

To excel in this role, you need to be familiar with parallelism, distributed computing, and low-level optimization. While it’s not mandatory to have ML knowledge, it’s beneficial to understand how ML algorithms work to optimize the infrastructure for specific ML models. Hence, companies prefer hiring engineers who are already skilled in infrastructure management and training them in ML.

ML Accelerator/Hardware Engineer

Hardware is a significant bottleneck for ML, as many ML algorithms are constrained by the limitations of processors. These include computational speed, memory storage capacity, and power supply. ML accelerator/hardware engineers are responsible for designing and building specialized hardware for ML, such as Graphical Processing Units (GPUs), Field Programmable Gate Arrays (FPGAs), and Application-Specific Integrated Circuits (ASICs).

To excel in this role, you need to have expertise in ML algorithms and an understanding of processor design. Your responsibilities include deciding which ML models to focus on, implementing and optimizing these models on specialized hardware, and benchmarking the performance of these models. Additionally, more hardware companies are incorporating ML algorithms to improve their chip design process, making this a vital role in ML production.

ML Solutions Architect

ML solutions architects are responsible for working with clients to understand their unique use cases and requirements and develop solutions that align with their needs. This role is prevalent in companies that offer products and services to other companies that use ML. As an ML solutions architect, you’ll work with clients to determine how your service or product can help with their ML needs.

To excel in this role, you need to have strong communication skills and a deep understanding of ML algorithms, data structures, and system architecture. Your responsibilities include designing and presenting ML solutions to clients, ensuring these solutions meet their needs, and providing technical support to clients during the implementation of these solutions.

Developer Advocate and Developer Programs Engineer

Developer Advocate and Developer Programs Engineer roles are responsible for bridging communication between people who build ML products and developers who use these products. These roles involve being the first users of ML products, writing tutorials, giving talks, and collecting and addressing feedback from the community. Developer Advocate and Developer Programs Engineer roles are essential to the success of ML startups that follow the open-core business model.

To excel in these roles, you need to have both excellent engineering skills and strong communication skills. Your responsibilities include building relationships with the developer community, educating them on the use of ML products, and gathering feedback to improve these products.

Conclusion

The roles mentioned above are essential to the success of ML production. While some of these roles don’t require specific ML knowledge, having an understanding of ML algorithms, data structures, and system architecture can be beneficial. If you’re interested in pursuing a career in ML, these roles provide an excellent opportunity to explore the industry and find your niche.