The field of artificial intelligence and machine learning has exploded in recent years, creating one of the most sought-after roles in tech: the machine learning engineer. Yet despite its popularity, many people still don't fully understand what this role entails. If you're considering a career in machine learning, this comprehensive guide will walk you through the day-to-day realities, responsibilities, and rewards of being a machine learning engineer.
Understanding the Machine Learning Engineer Role
What Is a Machine Learning Engineer?
Due to the rapid acceleration of AI and machine learning in recent years, the machine learning engineer role remains somewhat undefined and often varies between companies and geographies. However, it can generally be summarized as someone who uses software engineering, statistics, and machine learning skills to build and deploy algorithms into production.
The key distinction between a machine learning engineer and a data scientist lies in deployment capabilities. While there's often significant overlap between these roles at some companies, machine learning engineers specifically know how to deploy solutions to production-something that data scientists typically cannot do on their own.
Why This Role Emerged
The need for machine learning engineers only emerged in the last few years. Originally, it was the data scientist's job to build machine learning and statistical models. However, these models were often built in environments like Jupyter notebooks, which have zero business value on their own. There needed to be a role that combined the software engineering aspect with the data science aspect, and that's where the machine learning engineer sits-right in the middle.
The machine learning engineer's job is essentially to bring algorithms to life so they can generate tangible business value. This requires a unique blend of skills that bridges the gap between theoretical model development and practical, production-ready systems.
Core Responsibilities and Required Skills
Main Responsibilities
The primary responsibilities of a machine learning engineer include:
- Training, building, and deploying machine learning models
- Transforming research prototypes into production-ready systems
- Ensuring models generate measurable business value
- Collaborating with cross-functional teams to implement solutions
Essential Technical Skills
To succeed as a machine learning engineer, you need to be well-versed in both data science and software engineering. The technical stack includes:
- Programming Languages: Python, SQL
- Cloud Platforms: AWS and other cloud services
- Version Control: Git and GitHub
- Containerization: Docker
- Orchestration: Kubernetes
- Command Line: Bash and Zsh shell
- Machine Learning Frameworks: Various ML libraries and tools
This comprehensive skill set covers everything from handling raw data to deploying sophisticated models in production environments.
Experience Requirements
Machine learning engineer is not an entry-level role. You'll typically need a few years of experience as either a data scientist or software engineer first, then upskill yourself in the complementary areas. This foundation is crucial because the role demands both deep technical knowledge and practical experience in building production systems.
The Typical Machine Learning Workflow
Project Lifecycle
Most machine learning projects follow a standard workflow that ensures systematic development and deployment:
Ideation Phase: Someone proposes an idea or hypothesis about how to improve existing models. This could involve adding new features, trying different algorithms, or optimizing current solutions.
Data Availability Check: The team verifies whether the data needed to prove or disprove the hypothesis is readily available. This step prevents wasted effort on projects that lack necessary data infrastructure.
Research and Testing: If data is available, research begins immediately. The team tests whether new features or approaches actually improve model performance through rigorous experimentation.
Analysis and Validation: Results are thoroughly analyzed to confirm whether the model has genuinely improved. This involves statistical testing and business impact assessment.
Productionization: If improvements are validated, the solution is deployed to production, making it live for customers and generating real business value.
Collaborative Nature of the Work
Throughout this process, there's extensive interaction with various team members and roles. Machine learning engineering is far from a solitary endeavor:
Product Managers collaborate during the ideation phase, bringing business insight that helps identify opportunities engineers might miss. They ensure projects align with broader business objectives and customer needs.
Data Analysts and Engineers assist during data gathering and analysis stages, ensuring the team uses correct data sources and helping build ETL (Extract, Transform, Load) pipelines. They verify data quality and prevent issues that could compromise model performance.
Data Scientists contribute their statistical and machine learning expertise during the research phase, helping build robust models grounded in sound methodology.
Software Engineers partner during the deployment phase, ensuring models are deployed using software engineering best practices and that code is completely robust and maintainable.
Different Working Models for ML Engineers
The Embedded System
This is the most common approach in industry. As an embedded machine learning engineer, you work within a cross-functional team responsible for a specific domain. For example, you might be one of several machine learning engineers in a team specializing in classical machine learning and optimization problems, alongside data scientists, software engineers, a product manager, and data analysts.
This embedded approach offers significant learning opportunities from diverse team members and is the standard model across most companies. The cross-functional nature means everyone brings different skills and perspectives toward achieving shared business goals.
The Internal Consultancy Model
This represents the complete opposite of the embedded system. Here, you're part of a group of machine learning engineers acting as an in-house consultancy. Instead of being embedded in one team, you're deployed to different parts of the business to solve the highest-priority problems at any given time.
You might work on recommendation systems one quarter and optimization models the next, moving wherever the business needs are greatest. While this prevents deep specialization, it offers exposure to diverse problems and technologies.
The Infrastructure Team
In this model, machine learning engineers focus on building the platform and infrastructure rather than solving specific business problems. You develop tools, deployment processes, and technologies that streamline how other machine learning engineers deploy their models to production.
This work is crucial for scaling machine learning across an organization, even if it's less directly connected to business outcomes.
All three approaches have their pros and cons, and none is inherently better than the others. Personal preference matters, but you're largely at the mercy of how your employer structures their organization. Regardless of the model, you'll still do exciting and impactful work.
A Realistic Day in the Life
Dispelling the Myths
People online often glamorize working in tech, portraying it as endless coffee breaks, casual chats, coding just an hour a day while earning six figures. While the reality isn't quite that idyllic, working in tech is genuinely pleasant compared to many other professions.
Typical Daily Schedule
Here's what a standard workday looks like for a junior to mid-level machine learning engineer:
9:00 - 9:30 AM: Morning standup meeting to discuss yesterday's accomplishments and today's plans. This keeps the team aligned and identifies any blockers early.
9:30 - 10:30 AM: Additional meetings such as all-hands gatherings, data science syncs, or stakeholder meetings. These ensure cross-team coordination and strategic alignment.
10:30 AM - 1:00 PM: Deep coding block for focused work on assigned projects. This uninterrupted time is crucial for making meaningful progress on complex problems.
1:00 - 2:00 PM: Lunch break to recharge and socialize with colleagues.
2:00 - 5:30 PM: Afternoon deep work session, typically free of meetings. This extended block allows for sustained concentration on challenging technical problems.
5:30 - 6:00 PM: Wrap-up time to answer messages, respond to emails, and prepare for the next day before heading home.
Every day differs slightly, but this general flow is typical for most days. There's nothing extraordinary or as glamorized as social media might suggest, but it's a balanced and sustainable routine.
It's worth noting that this schedule reflects an individual contributor role without management responsibilities. Senior engineers and managers typically have more meetings and increased interaction with senior stakeholders, so their days look quite different.
Compensation and Career Prospects
Salary Expectations
It's no secret that people in tech generally earn significantly more than average. According to Levels.fyi, the median UK machine learning engineer earns approximately £93,000 per year, which is substantially above the national average. In other markets like the United States, compensation can be even higher, particularly at major tech companies.
This strong compensation reflects the specialized skill set required and the business value that machine learning engineers create through their work.
Industry Versatility
One of the most attractive aspects of being a machine learning engineer is the ability to work across numerous industries:
- Insurance and financial services
- E-commerce and retail
- Supply chain and logistics
- Healthcare and pharmaceuticals
- Technology and software companies
- Consulting firms
- Banking and fintech
Pretty much every industry now employs machine learning engineers, allowing you to choose a career path that aligns with both your technical interests and preferred business domain. While specializing in a particular industry can benefit long-term career progression, this flexibility is especially valuable at the entry and mid-levels.
Why Choose Machine Learning Engineering?
Intellectual Stimulation
Unless you've been living under a rock, you know that AI and machine learning are massive trends today. As a machine learning engineer, you work directly with the latest technologies and trends constantly. While keeping up with rapid developments can sometimes be exhausting, it's incredibly exhilarating to be at the forefront of technological innovation.
Every day brings something new-a breakthrough algorithm, a novel application, or a revolutionary approach. Being in the headwind of these developments makes coming to work genuinely exciting.
Work-Life Balance
Good work-life balance is common across tech and definitively better than in demanding fields like law, banking, and consultancy. Most tech workers maintain a 9-to-6 schedule, though hours can be flexible depending on project needs.
You'll typically work from home several days per week, providing flexibility to pursue other interests. This balance enables activities outside work-whether that's side projects, hobbies, sports, or spending time with family and friends.
Meaningful Impact
Machine learning engineers create solutions that directly improve customer experiences and generate measurable business value. Whether you're optimizing recommendation systems, improving operational efficiency, or building entirely new capabilities, your work has tangible impact.
This combination of technical challenge and real-world application makes the role deeply satisfying for those who want their work to matter.
Conclusion
The machine learning engineer role sits at the exciting intersection of data science and software engineering, combining statistical knowledge with production deployment skills. While it requires substantial experience and a broad technical skill set, it offers intellectual stimulation, strong compensation, excellent work-life balance, and the opportunity to work on cutting-edge technology that shapes the future.
The reality of the job is less glamorous than social media might suggest-it involves real work, collaboration, problem-solving, and occasional frustration. However, for those passionate about AI and machine learning, it remains one of the most rewarding career paths available in today's technology landscape.
If you're considering this career path, focus on building a strong foundation in either data science or software engineering first, then systematically develop complementary skills. With dedication and the right learning resources, you can position yourself for success in this dynamic and rapidly evolving field.
