Machine Learning (ML) Engineer

A Machine Learning (ML) Engineer is an AI engineer who can fulfill an ML Engineer job (mainly performs ML engineering tasks to deliver ML systems).

• AKA: ML Practitioner, ML Developer, Machine Learning Developer.
• 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 through computer science programs and specialized certifications.
  • They can (typically) be associated to a ML Engineering Job Description with specific requirements.
  • They can (typically) have an ML Engineering Job Level (see: ML engineering skill level).
  • They can (typically) be led by an ML Engineering Manager in technical organizations.
  • They can (typically) have high ML Problem Solving Skill through algorithm design and model optimization.
  • They can (typically) use an ML Development Environment with specialized tools.
  • They can (typically) perform Model Development through training pipelines and deployment workflows.
  • They can (typically) conduct Model Evaluation using performance metrics and validation techniques.
  • They can (typically) implement ML System Architecture for production deployment.
  • ...
  • They can (often) make use of an ML Platform System for model training.
  • They can (often) be a ML-Subdomain Expert, such as: an NLP Engineer, Vision Engineer, Reinforcement Learning Engineer.
  • They can (often) collaborate with ML Platform Engineers, Data Engineers, DevOps Engineers, and Research Scientists.
  • They can (often) have ML Engineering Education from academic programs or industry certifications.
  • They can (often) go through an ML Engineer Job Interview with technical assessments.
  • They can (often) develop Custom ML Solutions for specific business problems.
  • They can (often) maintain ML Infrastructure through scalable systems.
  • They can (often) implement MLOps Practices for continuous delivery.
  • They can (often) perform Model Monitoring in production environments.
  • ...
  • 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 range from being a Research-Focused ML Engineer to being a Production-Focused ML Engineer, depending on their role emphasis.
  • ...
• Examples:
  • Industry ML Engineers, such as:
    • SIE ML Engineers for gaming systems.
    • Autonomous Vehicle ML Engineers for self-driving systems.
    • Healthcare ML Engineers for medical applications.
    • Financial ML Engineers for trading systems.
  • Domain-Specialized ML Engineers, such as:
    • GenAI Engineers for large language models.
    • NLP Engineers for text processing systems.
    • Computer Vision Engineers for image analysis systems.
    • Speech Recognition Engineers for audio processing systems.
  • Platform ML Engineers, such as:
    • Cloud ML Engineers for distributed systems.
    • Edge ML Engineers for embedded systems.
    • MLOps Engineers for deployment pipelines.
    • Mobile ML Engineers for on-device systems.
  • Framework-Specialized ML Engineers, such as:
    • TensorFlow Engineers for Google ecosystem.
    • PyTorch Engineers for Meta ecosystem.
    • JAX Engineers for research implementations.
    • ONNX Engineers for model conversion.
  • ...
• Counter-Examples:
  • Applied Machine Learning (ML) Scientists, who focus on research advancement.
  • Data Analyst/Scientists or Research Data Scientists, who focus on data analysis.
  • Data Engineers, who focus on data pipelines.
  • Predictive Modelers, who focus on statistical modeling.
  • Distributed Systems Engineers, who focus on system architecture.
  • Software Engineers, who may lack ML expertise.
  • Research Scientists, who focus on theoretical advancement.
  • DevOps Engineers, who focus on infrastructure management.
• See: ML Engineering, ML-based System, Back-End Software Engineer, MLOps Platform, Model Registry, Feature Store, Experiment Tracking System.

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References

• https://linkedin.com/jobs/machine-learning-engineer-jobs/

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. ...

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: ...

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.”

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