Machine Learning (ML) Engineer
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A Machine Learning (ML) Engineer is a AI engineer who can fulfill an ML Engineer job (mainly performs ML engineering tasks to deliver ML systems).
- Context:
- They can (typically) be a member of a ML Engineering Workforce (within a ML engineering labor market).
- They can (typically) have ML Engineering Education.
- They can (typically) be associated to a ML Engineering Job Description.
- They can (typically) have an ML Engineering Job Level (see: ML engineering skill level).
- They can (typically) be led by an ML Engineering Manager.
- They can (typically) have high ML Problem Solving Skill.
- They can (typically) use an ML Development Environment.
- They can (often) make use of an ML Platform System.
- They can (often) be a ML-Subdomain Expert, such as: an NLP Engineer, Vision Engineer, ...
- They can (often) collaborate with ML Platform Engineers, Data Engineers, ...
- They can (often) have ML Engineering Education.
- They can (often) go through an ML Engineer Job Interview.
- They can range from being a Entry-Level ML Engineer (such as an ML Engineering Intern) to being an Experienced ML Engineer (a Senior ML Engineer, a Staff ML Engineer, a Principal ML Engineer, depending on their ML engineering skill level and ML programming experience.
- They can range from being an Product Team-Embedded ML Engineer (within a software engineering team) to being an ML Team ML Engineer (in an ML Engineering team).
- They can adhere to ML Engineering Best-Practices.
- They can go through phases such as ML Developer Onboarding, and ML Developer Offboarding.
- …
- Example(s):
- an SIE ML Engineer: [1], ...
- a GenAI Engineer, esp. one who specializes in training and fine-tuning GenAI models.
- an NLP Engineer who is expected to train NLP models.
- …
- Counter-Example(s):
- See: ML Engineering, ML-based System, Back-End Software Engineer.
References
2023
- web-search summary
- An ML (Machine Learning) Engineer is an advanced programmer who specializes in creating and managing software that incorporates artificial intelligence and machine learning. They handle large datasets and complex models to teach algorithms that yield beneficial outcomes and forecasts. Part of their duties encompass gathering and preparing data, constructing and training models, as well as launching them into production. ML Engineers collaborate with various experts like data scientists, data analysts, and IT professionals to create and execute solutions based on machine learning. They require a robust grasp of programming, expertise in big data analytics, and fluency in deep learning mechanisms and GPU hardware. This highly sought-after career offers attractive salaries, the prospect of advancement and creativity, and the capability to crack difficult problems.
2023
- chat
- Applied ML Research Scientists: They focus on developing machine learning algorithms and techniques tailored to address specific problems in clinical trials. Their success criteria include the ability to create innovative and effective models, the impact of their research on the company's products, and the improvements in the efficiency or accuracy of clinical trial processes.
- ML Engineers: They are responsible for implementing, optimizing, and deploying ML models in the clinical trial SaaS platform. Their success criteria include the efficiency and scalability of the models, the quality of the code they produce, and the seamless integration of these models into the company's existing infrastructure.
2021
- https://techspective.net/2021/02/20/what-is-the-role-of-a-machine-learning-engineer/
- QUOTE: ... To say that a machine learning engineer’s job is similar to a computer programmer is a dichotomy. While performing programming to an extent, a machine learning engineer’s task is to develop the machine to perform tasks without being explicitly told.
Computer programming takes rules and data, and then turning them into solutions. Meanwhile, machine learning takes solutions and data, and then turning them into rules. Furthermore, computer programming can develop a general-use calculator, while machine learning can develop one for a specific niche.
Machine learning engineers work closely with data scientists and software engineers. They create control models using data that are derived from the models defined by data scientists, allowing the machine to understand commands. From there, the software engineer designs the user interface from which the machine will operate.
The final product is software, like cnvrg MLOps, combining best practices from DevOps, software development and I.T. operations, and machine learning engineering. Organizations tend to spend more on infrastructure development when a machine learning-ready software can provide a precise estimate on how much they need. ...
- QUOTE: ... To say that a machine learning engineer’s job is similar to a computer programmer is a dichotomy. While performing programming to an extent, a machine learning engineer’s task is to develop the machine to perform tasks without being explicitly told.
2021
- "Google Computing Cloud Professional Machine Learning Engineer Certification."
- QUOTE: ... A Professional Machine Learning Engineer designs, builds, and productionizes ML models to solve business challenges using Google Cloud technologies and knowledge of proven ML models and techniques. The ML Engineer considers responsible AI throughout the ML development process, and collaborates closely with other job roles to ensure long-term success of models. The ML Engineer should be proficient in all aspects of model architecture, data pipeline interaction, and metrics interpretation. The ML Engineer needs familiarity with foundational concepts of application development, infrastructure management, data engineering, and data governance. Through an understanding of training, retraining, deploying, scheduling, monitoring, and improving models, the ML Engineer designs and creates scalable solutions for optimal performance.
The Professional Machine Learning Engineer exam assesses your ability to: ...
- QUOTE: ... A Professional Machine Learning Engineer designs, builds, and productionizes ML models to solve business challenges using Google Cloud technologies and knowledge of proven ML models and techniques. The ML Engineer considers responsible AI throughout the ML development process, and collaborates closely with other job roles to ensure long-term success of models. The ML Engineer should be proficient in all aspects of model architecture, data pipeline interaction, and metrics interpretation. The ML Engineer needs familiarity with foundational concepts of application development, infrastructure management, data engineering, and data governance. Through an understanding of training, retraining, deploying, scheduling, monitoring, and improving models, the ML Engineer designs and creates scalable solutions for optimal performance.
2017
- https://www.inc.com/jessica-stillman/jeff-bezos-this-is-how-to-avoid-regret.html
- QUOTE: At the center of any machine learning project lie the machine learning engineers. With backgrounds and skills in data science, applied research and heavy-duty coding, they run the operations of a machine learning project and are responsible for managing the infrastructure and data pipelines needed to bring code to production. Explains eBay VP of engineering Japjit Tulsi, machine learning engineers must be able to “straddle the line between knowing the mathematics and coding the mathematics.”