Senior ML Engineers

Employ Experienced Machine Learning Engineers

Over the last 10 to 20 years, machine learning has become a key player in the technology industry, and its popularity and growth are outstanding. As the industry expands at an impressive pace, machine learning has emerged as one of the top careers of 2022, and it is expected to keep expanding for years to come, indicating a bright future for the sector.

This is not an entry-level position due to the intricate nature of the job. To be a Machine Learning Engineer one must demonstrate a high level of proficiency in data science and software engineering and should possess relevant qualifications, skills, and experience. As a result, there is always high demand for Machine Learning Engineers who are sought after in a broad range of industries, including IT, marketing, finance, and business.

Responsibilities of a Senior Machine Learning Engineer

Machine Learning is an automated process that enables machines to learn and enhance their performance independently without human intervention. Engineers develop algorithms for the machines to analyze prior data, leading to more efficient and effective learning, resulting in improved outcomes over time.

As we head into 2022, the integration of machine learning into the workforce is expected to create numerous lucrative job opportunities, suitable for both new and experienced professionals alike. Leading corporations such as Google, Apple, Amazon, and others are looking for experienced machine learning developers to help achieve their business goals. A machine learning expert typically designs and implements machine learning algorithms. Recent graduates usually start their career as a machine learning engineer, data analyst, or data scientist, whereas experienced professionals may explore career paths in the following fields:

  • Senior Machine Learning Engineer
  • Senior Data Scientist
  • Lead Data Architect

Responsibilities and Obligations of a Senior Machine Learning Engineer

A Senior Machine Learning Engineer is typically tasked with developing and maintaining machine learning models and algorithms to solve different problems. They create and design new models and algorithms, train them on existing data, and evaluate model performance. Additionally, they are responsible for monitoring and optimizing existing models, contributing to data infrastructure development, and working with other engineers and researchers to create new models and algorithms. As a leader, they may lead a team to develop new machine learning products, create processes for deploying models, and oversee the development of data pipelines. Ultimately, the Senior Machine Learning Engineer is responsible for the successful execution and maintenance of machine learning-based solutions.

  • Should possess broad knowledge of machine learning techniques and alternative approaches.
  • Mentor and assist junior Machine Learning Engineers.
  • Conduct machine learning experiments and tests.
  • Partner with Data Scientists to address current challenges.
  • Inspect and ensure the quality of software code.
  • Should stay up-to-date with the latest ML technologies and trends.
  • Should have the ability to lead machine learning projects.
  • Contribute to the development of machine learning applications.

What is the path to becoming a Senior Machine Learning Engineer?

To become a Senior Machine Learning Engineer, an individual must possess a strong command of data science, computer languages, and mathematics. It is highly recommended to have an undergraduate degree in computer science or a related field to qualify for this position. Additionally, an individual should have at least five years of professional experience in the field as well as a comprehensive understanding of data. In order to remain competitive in the job market and remain current with the latest advancements and algorithms in the field, a Senior Machine Learning Engineer must stay up-to-date with the latest developments by attending conferences and seminars related to Machine Learning and Data Science. Practical experience in various projects is also helpful in achieving employment goals and success in the field.

Requirements for becoming a Senior Machine Learning Engineer

To be eligible for a role as a Senior Machine Learning Engineer, candidates must have the required number of years of experience in the field. Additionally, successful applicants must possess expertise in a variety of skill sets, which may include but are not limited to:

  1. Programming Language Proficiency

    Comprehensive knowledge of various programming languages, such as Python, Java, SQL, Scala, and PHP, is essential for Senior Machine Learning Engineers to develop applications successfully. Additionally, the engineer must possess expertise in mathematics, statistics, and data science to complement this proficiency.
  2. Software Development

    Understanding the interactions between various components is crucial for Machine Learning Engineers to produce effective outcomes and be prepared to address any issues that arise during the production process. Additionally, proficiency in REST APIs, libraries, and queries is essential. For Senior Machine Learning Engineers, expertise in system architecture, version control, and documentation can also be helpful in successfully fulfilling the requirements of the job.
  3. Machine Learning Algorithms

    A comprehensive understanding of various machine learning techniques available through application programming interfaces (APIs), libraries, and packages is essential for Machine Learning Engineers. It is also important to be aware of the advantages and disadvantages associated with different strategies, such as underfitting and data leakage. Participating in online forums, such as Kaggle, can be a helpful way to stay current on industry trends, troubleshoot challenges, and discover solutions.
  4. Data Modelling

    The process of creating data models through the application of various methodologies is known as data modelling. Data models are stored in databases and are crucial for analyzing data necessary for carrying out business operations. Evaluating the quality of a data model is an essential aspect of the process, considering factors such as accuracy and precision.
  5. Problem-Solving Skills

    Outstanding problem-solving skills are essential for Machine Learning Engineers due to the inherent challenges of the job. As a Senior Machine Learning Engineer, it is your responsibility to guide and mentor junior engineers and assist them in resolving any problems that may arise.

What is the pathway to secure a job as a remote Senior Machine Learning Engineer?

To achieve a senior position in the field of machine learning engineering, acquiring substantial experience in the ML engineering domain is crucial. In addition, staying updated with the latest developments in the machine learning and data science industries is essential. Work provides developers with opportunities to secure remote employment aligned with their career aspirations, as well as opportunities for professional growth by collaborating with leading companies in Silicon Valley. If you want to take advantage of this opportunity, you can become a part of Work’s global developer network and advance your career.

Description of the Job

Job Responsibilities

  • Analyze user behavior, participate in project brainstorming, and translate analytical use cases into comprehensive technological end-to-end solutions.
  • Designing and implementing large-scale machine learning algorithms, data pipelines, and back-end services.
  • Developing and training models to create high-quality machine learning applications.
  • Maintaining up-to-date knowledge of the latest advancements in the fields of AI-ML and NLP.
  • Engaging in all stages of the development process and taking responsibility for challenging projects.
  • Defining metrics and conducting A/B tests to evaluate the impact of the process.
  • Providing mentorship and guidance to junior ML engineers.
  • Ensuring technical and complex topics are comprehensible to non-technical users.


  • A Bachelor’s or Master’s degree in Engineering, Computer Science, or Statistics (or equivalent experience) is necessary.
  • Requires a minimum of 5 years of practical experience in machine learning, with rare exceptions for exceptionally talented developers.
  • Pre-existing familiarity with natural language processing and machine learning techniques is mandatory.
  • Proficiency in programming languages such as Python, Java, R, PHP, Scala, and other relevant languages.
  • Experience working with deep learning frameworks like TensorFlow and Pytorch, along with libraries like scikit-learn.
  • Experience with data processing frameworks like Apache Spark, Flink, and other relevant frameworks.
  • Ability to understand concepts such as data structures, neural networks, data modelling, and software architecture is crucial.
  • Proficiency in English is necessary for effective communication.
  • Work on a full-time basis (40 hours per week) with a 4-hour overlap with US time zones.

Desirable skills

  • Proficiency in designing and training machine learning models.
  • Extensive expertise in graph learning, MATLAB, SQL, and AWS technologies.
  • Proficiency in Git, Github, and CI/CD concepts.
  • Ability to build, develop, and oversee recommendation services.
  • Outstanding analytical and problem-solving skills.
  • Ability to work independently and take on responsibilities.
  • Exceptional communication and leadership skills.


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What makes Works Senior ML Engineers different?
At Works, we maintain a high success rate of more than 98% by thoroughly vetting through the applicants who apply to be our Senior ML Engineer. To ensure that we connect you with professional Senior ML Engineers of the highest expertise, we only pick the top 1% of applicants to apply to be part of our talent pool. You'll get to work with top Senior ML Engineers to understand your business goals, technical requirements and team dynamics.