Machine Learning Engineer Salary and Job Description

https://ift.tt/LnI0wc1 Machine learning (ML) engineers are the masterminds behind intelligent systems that learn and improve over time. The...

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Machine learning (ML) engineers are the masterminds behind intelligent systems that learn and improve over time. They power everything from Netflix recommendations to self-driving cars. As more companies scramble to take advantage of AI and ML solutions, the demand for these professionals continues to rise.

Machine learning engineer employment prospects

According to the U.S. Bureau of Labor Statistics, jobs for computer and information research scientists, including ML engineers, will grow by 26% between 2023 and 2033. Businesses seeking a competitive edge through smart decision-making and automation drive this growth.

With the increasing reliance on data-driven decision-making, the need for skilled machine learning engineers has never been greater. Companies are eager to hire machine learning developers who can design and implement sophisticated algorithms to process and interpret vast amounts of data. Whether it’s enhancing customer experiences or optimizing operational efficiencies, the expertise of machine learning developers is invaluable.

What to expect

If you're excited by the prospect of a career that is both intellectually stimulating and highly impactful, machine learning engineering could be the perfect fit. In this article, we'll explore what it's really like to work as a machine learning engineer, the salaries you can expect, and the key skills you'll need to break into this lucrative field.

What does a machine learning engineer do?

On a day-to-day basis, machine learning engineers are in the trenches, building and deploying models that power intelligent systems. They wrangle datasets, design experiments, test algorithms, and fine-tune models for optimal performance. For example, an ML engineer at a social media company might develop a new recommendation system to keep users engaged. To excel in this role, machine learning engineers need both technical and soft skills. Some of the key technical skills include:

• Machine learning frameworks (e.g., TensorFlow, PyTorch)
• Data preprocessing and feature engineering
• Model evaluation and validation techniques
• Programming languages like Python

Required soft skills

Beyond coding, machine learning engineers must be strong communicators who can collaborate with cross-functional teams to ship successful projects. They should be able to break down complex technical concepts for non-tech folks and keep everyone aligned. Since the ML world moves rapidly, a love for constant learning should appeal to you.

Many ML engineers have studied computer science, math, or statistics, but alternative paths exist. Dataquest's Machine Learning in Python skill path provides hands-on opportunities to build your skills in order to land your dream job and secure a competitive machine learning engineer salary.

Machine learning engineer salary and career outlook

As of this writing, Glassdoor data showed that the median total annual salary for ML engineers in the United States was an impressive \$169,000, encompassing a total pay range varying from \$134,000 up to \$215,000 per year. Please note that salary data may have changed slightly since this article was last updated.

Naturally, several factors can significantly augment the salary of a machine learning engineer:

• Location: ML engineers in tech hubs like San Francisco, New York, and Seattle tend to command the big bucks. Salaries in Silicon Valley, for example, can far exceed national averages due to the high demand (and cost of living).
• Industry matters: Tech companies and AI-focused startups often shell out the most for top ML talent. Think of firms like Google, OpenAI, or Tesla where machine learning is at the core of the business – they offer premium compensation to attract the best.
• Experience pays: According to available Glassdoor data at the time of this writing, junior ML engineers with less than a year of experience had an estimated total pay of around \$133,000 per year, while senior ML engineers could expect to rake in an average of \$233,000 annually. Please note that salary data may have changed slightly since this article was last updated. Clearly, as you gain experience and expertise, your earning potential skyrockets.

So what's the typical career trajectory for a machine learning engineer? Most start out in junior roles, then climb the ladder to mid-level and senior positions – and eventually titles like Lead ML Engineer, Machine Learning Architect, or Head of Machine Learning. The key to moving up (and boosting your machine learning engineer salary) is to constantly sharpen your skills and take on increasingly complex projects.

Specializing in demanding areas like deep learning, natural language processing (NLP), computer vision, or generative AI can also give you a serious edge. While an advanced degree (Master’s or PhD) can help for certain research-heavy roles, real-world experience and an outstanding portfolio often are the tickets to the top. And with resources like Dataquest's Supervised Machine Learning in Python course, you can gain the hands-on skills to fast-track your career growth.

Junior machine learning engineer job description

A junior machine learning engineer position provides an exciting launchpad into the growing field of AI and ML. In this role, you'll work with vast datasets, create intelligent systems, and directly impact an organization's ability to leverage data from day one.

Common job titles:

• Junior Machine Learning Engineer
• Machine Learning Developer (Entry Level)
• Associate Machine Learning Engineer
• AI/ML Software Engineer (Entry Level)

As a junior ML engineer, you'll collect, process, and analyze complex data to build and deploy models. This role is perfect for people who love algorithms and enjoy tackling complex problems. You should be comfortable turning the latest research into real-world solutions under the guidance of senior engineers.

Most junior positions look for candidates with a Bachelor's degree in a field like Computer Science, Data Science, Mathematics, or Statistics. Hiring managers also love to see hands-on experience through internships, personal projects, or reputable online courses, as it shows you can apply your skills to solve real-world problems.

Junior machine learning engineer salary: At the time this post was written, the typical total salary range for a junior machine learning engineer with less than a year of experience ranged from \$125,000 to \$208,000 per year according to Glassdoor. Please note that salary data may have changed slightly since this article was last updated.

Key responsibilities:

• Collecting and preprocessing large datasets for model training – e.g. writing data pipeline code to gather data and clean it for use.
• Designing, implementing, and testing ML models using frameworks like TensorFlow or PyTorch
• Conducting experiments to tune hyperparameters and improve model performance.
• Collaborating with cross-functional teams (data scientists, software engineers, product managers) to integrate ML solutions into products.
• Staying up-to-date with the latest research and advancements in the field – and eagerly learning new techniques.

Must-have skills:

• Strong programming skills in Python and familiarity with ML libraries (scikit-learn, pandas, etc.).
• Solid understanding of fundamental machine learning algorithms and concepts (regression, classification, clustering, etc.).
• Experience with data manipulation and feature engineering (handling missing data, encoding categorical variables, etc.).
• Knowledge of software engineering best practices like version control (Git) and unit testing.
• Excellent problem-solving abilities and communication skills – being able to explain your models and results to non-experts is important even at junior levels.

Pro Tips

While not always “required,” familiarity with cloud ML platforms (AWS SageMaker, Google Cloud AI, etc.) and basic MLOps tools can be a plus for junior candidates. It shows you understand how models are deployed in real-world settings.

When applying for junior ML engineer positions, be sure to highlight relevant coursework, projects, and internships on your resume. Showcase any hands-on projects where you built an ML model or analyzed a dataset – this can set you apart. During interviews, be prepared to discuss the technical details of projects (e.g. how you tuned a model or overcame a data challenge) and demonstrate your enthusiasm for continuous learning.

Mid-level machine learning engineer job description

Take your machine learning career to the next level with a mid-level machine learning engineer position. In this role, you'll serve as the bridge between junior engineers building initial models and senior staff managing production systems. Your work will ensure seamless integration and optimal performance of ML systems at scale.

Common job titles:

• Mid-Level Machine Learning Engineer
• Senior Machine Learning Developer (mid-level in some orgs)
• Lead Machine Learning Engineer (often mid-level technical lead)
• AI/ML Software Engineer (Mid Level)

As a mid-level ML engineer, you'll tackle more complex challenges, such as optimizing algorithms for speed or scalability, troubleshooting model performance issues in production, and guaranteeing smooth integration between ML components and the broader software infrastructure. This position demands a balance of advanced technical skills and effective collaboration abilities to translate prototype models into production-ready solutions.

Your typical day might involve enhancing the accuracy and efficiency of existing ML models, refactoring code for deployment, and integrating ML systems with other backend services or APIs. You’ll likely participate in code reviews, design discussions, and sprint planning, while also mentoring junior colleagues and sharing your expertise through knowledge-sharing sessions.

Mid-level machine learning engineer salary: According to Glassdoor at the time of this writing, the typical salary range for a mid-level machine learning engineer (4-6 years experience) was between \$133,000 and \$230,000 per year (varying by location and company size). Mid-level practitioners in major tech companies or high-cost regions will skew toward the upper end of that range. Please note that salary data may have changed slightly since this article was last updated.

Key responsibilities:

• Optimizing and fine-tuning ML models for better performance and efficiency (e.g., reducing model latency or improving accuracy on edge cases).
• Integrating ML systems with other software components – ensuring that machine learning pipelines play nicely within the overall product architecture.
• Ensuring a smooth transition from prototype to production environments, including setting up model deployment pipelines and monitoring solutions.
• Actively participating in code reviews and design discussions to maintain high code quality and robust ML engineering practices.
• Mentoring junior team members and sharing knowledge through pair programming, presentations, or tech talks.

Must-have skills:

• Strong software engineering skills in languages such as Python (and often one other language like C++ or Java for certain applications).
• Proven track record of working with popular ML frameworks (TensorFlow, PyTorch, scikit-learn) to build and deploy models.
• In-depth knowledge of data engineering principles (ETL pipelines, data storage solutions) and possibly experience with big data tools (e.g., Spark) for handling large datasets.
• Familiarity with MLOps practices – deploying models as microservices, using Docker/Kubernetes, setting up CI/CD for ML, and monitoring model performance in production.
• Exceptional problem-solving abilities and communication skills. Ability to thrive in a collaborative team environment and effectively mentor junior engineers.

When applying for mid-level ML engineer positions, highlight your most impactful projects and the results you've achieved. For example, did you deploy a model that improved conversion rates by 10

If you're driven to advance your machine learning career and make a significant impact in the AI/ML space, a mid-level ML engineer position could be an ideal next step. It’s an opportunity to tackle complex challenges, collaborate with talented peers, and build innovative solutions that shape the future of machine learning in industry.

Senior machine learning engineer job description

As a senior machine learning engineer, you'll be at the forefront of driving business innovation through cutting-edge AI technology. You’ll take the lead on complex projects, craft strategic ML roadmaps, and mentor team members to help them level up their skills. Senior ML engineers often function as technical architects and team leaders, ensuring that an organization’s ML efforts align with business goals and are executed at the highest standard.

Common job titles:

• Senior Machine Learning Engineer
• Lead Machine Learning Engineer
• Principal Machine Learning Engineer
• Machine Learning Architect (or AI Architect)

What distinguishes a senior role is the exceptional combination of deep technical expertise, strategic thinking, and leadership. As a senior ML engineer, you won't just be building algorithms – you'll be setting the direction for how ML is used in products and possibly influencing business strategy with your insights. You might design the overall data/ML architecture for a new project, make decisions on build-vs-buy for ML solutions, and ensure that best practices (from coding standards to ethical AI considerations) are followed.

On any given day, you may lead a team to develop and deploy an innovative ML model into production. You might create a data architecture designed for speed, scale, and reliability to support machine learning workloads. Expect to mentor junior engineers, provide code reviews and guidance, and collaborate with product stakeholders or cross-functional leaders to align ML initiatives with company objectives. Senior ML engineers are often the go-to problem solvers for thorny issues and are responsible for keeping the team updated on the latest advancements (for example, evaluating how a new breakthrough in transformer models or generative AI could be leveraged by the business).

Senior machine learning engineer salary: According to available Glassdoor data at the time of this writing, the average base pay for a senior machine learning engineer was a cool \$233,000 per year, with a total pay range of \$192,000 to \$290,000, again depending on location and the size of the employer. Senior roles in Silicon Valley or at AI-first companies tend to be on the higher end of the spectrum (in some cases well beyond these figures, especially with stock), whereas those in smaller firms or lower cost areas may be towards the lower end (but still very well-paid)

Key responsibilities:

• Leading ML projects end-to-end: from high-level planning and model design to overseeing implementation and deployment.
• Designing and optimizing data/ML architectures that are built for scale, reliability, and maintainability. (For example, setting up data lakes, feature stores, or real-time model serving infrastructure.)
• Mentoring and guiding junior and mid-level team members, providing technical direction and code reviews to elevate the whole team’s capabilities.
• Defining ML strategy and communicating with stakeholders: translating complex ML concepts into business insights and getting buy-in for new initiatives. A senior ML engineer might present to executives on how a new AI model could open a market opportunity.

Must-have skills:

• Expertise in machine learning algorithms and modeling techniques – you should have a deep intuition for selecting the right approach (from linear models to deep neural networks) for a given problem, and know the state-of-the-art advances.
• Strong programming and system design skills – beyond Python, many senior ML engineers are proficient in languages like C++/Java or specialized tools, and can architect complex software systems. You should be able to code at an advanced level and guide others in writing maintainable code.
• Experience with MLOps and cloud infrastructure – you know how to deploy, scale, and monitor ML models in production environments (using AWS, GCP, Azure, Kubernetes, etc.), and understand how to build pipelines that automate the ML lifecycle.
• Project leadership and management abilities – capable of driving projects forward, making architectural decisions, and coordinating work across multiple people. Essentially, you can manage not just models, but also timelines and deliverables.
• Excellent communication and stakeholder management – able to articulate the value of ML solutions to non-technical stakeholders, write clear documentation, and perhaps even help in setting company-wide AI policies or best practices.

As a senior ML engineer, you’ll be the key to ensuring your organization harnesses the full power of machine learning to achieve its goals. You act as the bridge between the technical and strategic realms, driving initiatives that can take the business to new heights. In this role, staying current with cutting-edge research (and often participating in it) is part of the job – you might be prototyping applications of the latest AI models or evaluating new tools on the market.

If you're ready to take your machine learning career to the next level and make a real impact, a senior ML engineer position could be your dream job. Be prepared to put all your skills to the test: you’ll lead game-changing projects, mentor others, and help shape the future of AI and ML within your organization.

How to find and apply for jobs in machine learning engineering

As the demand for machine learning engineering expertise rises, securing a job in this field requires a strategic approach. Here are some effective strategies to help you navigate the job search process:

• Build a strong online presence: Maintain an up-to-date and professional LinkedIn profile, highlighting your machine learning engineering skills, projects, and achievements. Engage with relevant industry groups and share your knowledge through posts or articles.
• Leverage your network: Reach out to your professional contacts, such as former colleagues, classmates, or professors, and let them know you're actively seeking machine learning engineering opportunities. They may be aware of job openings or able to provide valuable referrals.
• Attend industry events and meetups: Participate in local or virtual machine learning engineering events, conferences, or meetups. These platforms provide opportunities to learn about job openings, network with professionals, and showcase your skills.
• Contribute to open-source projects: Actively contributing to open-source machine learning projects can demonstrate your technical abilities and passion for the field. It also allows you to gain practical experience and expand your portfolio.
• Tailor your application materials: Carefully review job descriptions and requirements, and customize your resume, cover letter, and portfolio to highlight how your qualifications align with the specific role and company.
• Prepare for technical interviews: Machine learning engineering interviews often involve coding challenges, technical discussions, and project presentations. Practice your Python skills and be prepared to showcase your problem-solving abilities and discuss your past projects in detail.

Remember, securing a machine learning engineering role requires persistence, continuous learning, and a proactive approach. Stay up-to-date with industry trends, expand your skillset, and actively engage with the machine learning community to increase your chances of success in this exciting and rapidly-evolving field.

Next Steps

Becoming a successful machine learning engineer requires a unique blend of skills, traits, and interests. If you enjoy problem-solving, have an aptitude for programming, and are curious about using data to drive innovation, this could be the perfect career path for you.

No matter where you are in your learning journey, there are steps you can take to move forward:

• Beginners: Learn the basics of machine learning workflows and algorithms.
• Intermediates: Build a portfolio that demonstrates your ability to solve real-world problems with machine learning.
• Advanced learners: Contribute to open-source projects or earn advanced certifications to take your expertise to the next level and showcase your commitment and skill to potential employers.

Throughout your career, tap into the support and networking opportunities offered by other communities. Engage with peers through online forums and local meetups to learn and stay current on industry trends.

The Machine Learning in Python skill path at Dataquest provides structured learning to help you develop practical skills through hands-on projects, with guidance from our knowledgeable community.

Regularly assess your skills and commit to continuous learning to keep pace with your goals and the evolving field. Becoming a skilled machine learning engineer is a journey of ongoing growth, real-world practice, and community engagement. By dedicating yourself to these principles, you'll build a fulfilling career at the forefront of technological innovation.

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