A Machine Learning Engineer is a professional who specializes in designing, implementing, and optimizing machine learning models and systems. They possess a strong background in mathematics, computer science, and data analysis, enabling them to develop intelligent algorithms that can learn and make predictions from data. In this blog post, we will describe the primary responsibilities and the most in-demand hard and soft skills for Machine Learning Engineers.

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Main Responsibilities of a Machine Learning Engineer

The following list describes the typical responsibilities of a Machine Learning Engineer:

Analyze

• Analyze and interpret complex data to identify patterns and insights.

• Analyze data from disparate sources including images, video, radar, signals and more.

• Analyze, visualize and model large datasets using R and Python.

Apply

• Apply machine learning and data science techniques and design distributed systems.

• Apply software development practices and standards to develop robust and maintainable software.

• Apply techniques for handling missing data and dealing with class imbalance.

Architect

Architect, build, test, deploy distributed, scalable, and resilient.

Ask

• Ask Data and see the answer.

• Ask hard questions and challenge assumptions to ensure that we're solving the right problems.

Assess

Assess and evaluate risk.

Assist with

Assist with APIs for ML model serving.

Assume

Assume responsibility for key parts of the Interos Knowledge Graph architecture.

Attend

Attend daily stand-up and periodic team meetings.

Automate

Automate processes within the development pipeline.

Build

• Build and maintain the infrastructure and CI / CD pipelines.

• Build data science applications related to text-based search, and audio & video recordings.

• Build infrastructure to support the evolution of our shopper interaction toolset.

• Build large-scale data pipelines and computation systems that process massive amount of data.

• Build, maintain and manage data pipelines that support the modelling initiatives of data scientists.

• Build platforms and infrastructure for data science at scale.

• Build processes and tools to help monitor and analyze performance and data accuracy.

• Build prototypes to explore novel methods / algorithms to improve the Statistical Language Models.

• Build tools and systems to scale deployment of models and pipelines.

Collaborate with

• Collaborate with a smart, passionate team to build powerful AI product.

• Collaborate with cross-functional teams to understand business objectives and requirements.

• Collaborate with data engineers to monitor data flow and data quality and keep data organized.

• Collaborate with Data Scientists to optimize research outcomes and models for production.

• Collaborate with domain experts to define relevant features and labels.

• Collaborate with other engineers across disciplines.

• Collaborate with other engineers to enable Apple products with efficient and accurate ML solutions.

• Collaborate with software engineers to integrate machine learning systems into existing software infrastructure.

• Collaborate with stakeholders to define project goals and success criteria.

Collect

Collect, clean, and preprocess large datasets for training and testing.

Communicate

• Communicate complex processes to business leaders.

• Communicate effectively and distill technical details into human terms.

• Communicate with internal teams and stakeholders to understand project requests and requirements.

Complete

• Complete documentation and procedures for installation and maintenance.

• Complete programming and implement efficiencies, performs testing and debugging.

• Complete User Acceptance Testing on new releases.

Conduct

• Conduct code reviews and provide feedback to improve code quality.

• Conduct experiments and evaluate model performance using appropriate metrics.

• Conduct modeling and experimental design.

• Conduct performance analysis and optimization of machine learning algorithms.

• Conduct research to stay updated on the latest advancements in machine learning techniques.

Connect

• Connect and collaborate with subject matter experts across multiple industries.

• Connect and manage IoT devices, networks and gateways.

Consult

Consult on statistical problems.

Contribute to

• Contribute to a culture of continuous improvement, and data driven results.

• Contribute to growth of analytic community.

• Contribute to the evaluation and selection of software product standards.

• Contribute to the formulation and implementation of IT strategy.

• Contribute to the full life cycle of ML models – data analysis, modeling, tuning & productization.

• Contribute towards continuous improvement across data and engineering department.

Coordinate

Coordinate the creation, validation, and use of models.

Create

Create future friendly® possibilities.

Define

Define new techniques and best practices for all areas of req and ML.

Deploy

• Deploy machine learning models into production environments.

• Deploy your algorithms to production to identify actionable insights from large databases.

Design

• Design and build highly scalable and robust machine learning pipelines.

• Design and build prototypes, proof of concepts and demos of Deep Learning applications.

• Design and deliver innovative AI solutions in various areas of the Boast.AI platform.

• Design and implement production level code.

• Design and implement software architecture that will allow scalability and maintainability.

• Design and optimize machine learning architectures and frameworks.

• Design ML / DL applications that demonstrate the technology's real-world potential.

• Design solutions that are enterprise scalable and maintainable.

Develop

• Develop and deploy ML / DL models from raw data.

• Develop and implement machine learning algorithms and models.

• Develop and maintain large scale ML Services.

• Develop and maintain machine learning libraries, tools, and frameworks.

• Develop and maintain ML platforms for Data Scientists to use.

• Develop and manage tools and applications with database backend.

• Develop and scale our data-driven systems.

• Develop appropriate automated tests, environments, and infrastructure.

• Develop data set processes for data modelling, mining and production.

• Develop / Design ADF pipelines.

• Develop documentation to share knowledge with other engineers.

• Develop effective implementation and procurement strategies, consistent with business needs.

• Develop high-performance, data-driven systems in a distributed infrastructure.

• Develop long-term strategic relationships with customers, partners, industry leaders and government.

• Develop ML applications according to product needs.

• Develop solutions for real world, large scale problems.

• Develop tools to visualize data.

Document

• Document and communicate the machine learning process, methodologies, and results.

• Document process, procedures, and training materials for staff and stakeholders.

Embody

Embody technical leadership, recommend and enforce best practices.

Enhance

• Enhance and develop solutions with a heavy focus on performance and efficiency.

• Enhance existing Computer vision systems to achieve high performance.

Ensure

• Ensure architecture will support the requirements of the business.

• Ensure data privacy and security when handling sensitive information.

• Ensure that we are continuously raising our standard of engineering excellence.

Evaluate

• Evaluate and select appropriate algorithms and models for specific tasks.

• Evaluate customer use cases from a technical and data-driven perspective.

• Evaluate, implement, and improve machine learning models.

Explain

Explain model behaviors to non-technical audience for model acceptance and model governance.

Explore

Explore, build and deploy machine learning pipelines at scale.

Extend

Extend and maintain existing machine learning algorithms.

Flesh out

Flesh out and iterate on requirements in collaboration with the product team.

Follow

Follow the links to review the EEO is the Law poster and its supplement.

Gain

Gain insights through research, our forums, and A / B tests.

Generate

Generate synthetic sensor data using simulators.

Help

• Help define and drive execution of the Machine Learning roadmap for Non Genuine Conversion program.

• Help design and execute our long-term Machine Learning strategy across Cash App.

Identify

Identify key variables, parameters and elements defining a problem or its solution.

Implement

• Implement data augmentation and data normalization techniques.

• Implement data pipelines and workflows for efficient data processing.

• Implement data visualization techniques to communicate insights effectively.

• Implement machine learning algorithms and customized libraries.

• Implement, scale, and maintain complex AI / NLP models.

Improve

• Improve existing Machine Learning models.

• Improve existing packages.

• Improve scalability, speed and performance of existing models.

Integrate

Integrate predictive results into application workflows.

Interface with

Interface with business areas to ensure all initiatives support business strategies and goals.

Keep

• Keep our data separated and secure across multiple accounts and environments.

• Keep up to date with advances in ML technologies and techniques.

Lead

• Lead and Handle full development cycle including review design and code.

• Lead and transform data science prototypes.

• Lead a talented team of 3-4 engineers in building and maintaining large scale ML services.

• Lead discussions at peer review and uses quantitative skills to positively influence decision making.

• Lead the performance and ongoing enhancements of your products.

Learn

Learn from your peers and grow into new opportunities.

Maintain

Maintain the existing production data pipelines by contributing directly to the Go codebase.

Manage

Manage the infrastructure and data pipelines needed to bring ML solution to production.

Mentor

Mentor and provide guidance to junior machine learning engineers.

Monitor

• Monitor and control costs within own work.

• Monitor and maintain deployed models, ensuring their reliability and accuracy.

Optimize

• Optimize and enhance computational efficiency of algorithms and software design.

• Optimize Machine Learning algorithms to run under latency constraints.

• Optimize model hyperparameters to improve performance and generalization.

• Optimize model workflow for elastic multi-node environments.

• Optimize software performance.

Oversee

Oversee end-to-end architecture of AI solutions.

Own

• Own problems end-to-end in a very ownership driven culture.

• Own the development process, from the conception of the models to their evaluation.

Participate in

• Participate in cutting edge research in machine learning applications.

• Participate in high quality processes including reviews, test, verification, and process improvement.

• Participate in software development as part of an AGILE team.

Perform

• Perform code reviews to guarantee high quality products moving to production.

• Perform data studies of new and diverse data sources.

• Perform feature engineering to extract relevant information from raw data.

• Perform statistical analysis and fine-tuning using test results.

• Perform troubleshooting and debugging of machine learning models and systems.

Prepare

Prepare 3rd-party license compliance.

Produce

Produce project outcomes and isolate issues.

Productionize

Productionize, deploy and monitor machine learning based services.

Productize

Productize AI models in the cloud and ensure its scalability.

Prototype

Prototype and release models to optimize the creation and delivery of Offers to shoppers.

Provide

• Provide expert guidance about implementing and optimizing GPU-based data processing.

• Provide technical consulting on complex projects.

• Provide technical guidance to your team.

Publish

Publish and present novel perception algorithms to internal and external communities.

Reach

Reach out to support and collaborate with other technology functional teams to build the solution.

Ready

Ready to build advanced forecasting tools for tens of thousands of users.

Research

• Research and implement novel ML and statistical approaches to add value to the business.

• Research and prototype new modeling ideas to continuously optimize model results.

Resolve

Resolve and implement unit testing and system level testing strategies.

Review

Review code and make recommendations for design and architectural changes.

Run

Run state of the art predictive models in production.

Secure

Secure IoT data with Blockchain.

Stay updated on

• Stay updated on best practices for model interpretability and explainability.

• Stay updated on emerging trends and advancements in machine learning.

Support

• Support ad-hoc requests and drive insights from the data.

• Support and enhance the existing work on health user profiling, prediction, and targeting tools.

• Support existing codebase for data integration and production support for our core models.

• Support our ecosystem with sample algorithms and libraries.

Test

• Test and debug software and hardware related issues.

• Test and validate predictive models.

• Test validate, and deploy algorithms into production.

Train

Train and fine-tune machine learning models using various techniques and algorithms.

Translate

Translate Data Science experimental code into tested, production ready modules.

Turn

Turn unstructured data into useful information by auto-tagging or other data cleansing methods.

Update

Update / retrain models following established tooling.

Verify

Verify model effectiveness.

Work with

• Work closely with data scientists and analysts to create and deploy new product features.

• Work closely with our compiler team to deploy your optimized models on mobile apps.

• Work collaboratively with biology subject matter experts to maximize impact of workstreams.

• Work directly with data scientists, cloud engineer, and other stakeholders.

• Work on backend and infrastructure to build, deploy, and serve machine learning models.

• Work on building large scale Statistical Language Models, a critical system in Speech Recognition.

• Work on our existing acceleration algorithms and framework.

• Work on search / information retrieval problems.

• Work with a team of 20 scientists and engineers to build the machine.

• Work with enthusiastic and dedicated people at the forefront of science and technology trends.

• Work with other teams to implement best practices for engineering and management.

• Work with our team of data scientists to build deployable machine learning software.

• Work with the c-level executives to create best practices and systems around data.

• Work with wider product teams to make the right data available for model building and analysis.

Write

• Write and design application services Needed for other engineering teams to use our processes.

• Write highly efficient code to serve and a / b testing new models.

• Write production-ready Python / c code.

Most In-demand Hard Skills

The following list describes the most required technical skills of a Machine Learning Engineer:

1. Proficiency in programming languages such as Python, R, or Java.

2. Strong knowledge of machine learning algorithms and techniques.

3. Experience with deep learning frameworks, such as TensorFlow or PyTorch.

4. Proficiency in using libraries for data manipulation and analysis, such as NumPy, Pandas, or Scikit-learn.

5. Knowledge of statistical analysis and hypothesis testing.

6. Familiarity with cloud platforms for machine learning, such as AWS or Google Cloud.

7. Understanding of natural language processing (NLP) techniques and frameworks.

8. Knowledge of computer vision algorithms and frameworks.

9. Experience with big data processing frameworks, such as Apache Spark or Hadoop.

10. Proficiency in SQL and relational databases for data querying and manipulation.

11. Familiarity with distributed computing and parallel processing.

12. Understanding of data structures and algorithms.

13. Experience with version control systems, such as Git.

14. Knowledge of software development practices and methodologies.

15. Understanding of optimization algorithms and techniques.

16. Familiarity with reinforcement learning concepts and algorithms.

17. Experience with model deployment and serving frameworks, such as Flask or Docker.

18. Knowledge of cloud-based machine learning services, such as Amazon SageMaker or Google Cloud AI Platform.

19. Understanding of data engineering concepts and techniques.

20. Proficiency in using data visualization tools, such as Matplotlib or Tableau.

Most In-demand Soft Skills

The following list describes the most required soft skills of a Machine Learning Engineer:

1. Strong analytical and problem-solving skills.

2. Excellent communication and presentation abilities.

3. Ability to work effectively in cross-functional teams.

4. Creativity and innovative thinking to explore new approaches and solutions.

5. Strong attention to detail and commitment to producing high-quality work.

6. Ability to handle and prioritize multiple projects and deadlines.

7. Continuous learning mindset to keep up with evolving technologies.

8. Strong organizational and time management skills.

9. Collaboration and teamwork abilities.

10. Adaptability and flexibility to work in a dynamic and fast-paced environment.

Conclusion

Machine Learning Engineers are at the forefront of developing intelligent systems and leveraging data to drive insights and decision-making. By possessing a combination of technical expertise, problem-solving skills, and effective communication abilities, these professionals contribute to the advancement of machine learning technologies and their successful integration into various domains and industries.