Our capable machine learning engineers can help you develop and deploy advanced algorithms that turn data into actionable insights. Whether you're building predictive models, process automations, or AI-powered apps, our ML engineers can deliver scalable and efficient machine learning applications.
Machine Learning Engineer
Europe
Trusted by
We’ve been doing this longer than almost any company in the industry. Our extensive network of remote developers enables us to find, evaluate, and place high-quality candidates with the technical skills and culture fit you’re looking for.
We combine powerful technology and a human interview process to ensure we identify, evaluate, and present you with only the most qualified Machine Learning engineers candidates.
Share your requirements
In-depth, live vetting
Choose your developer
Onboarding & tracking with our support
There are over 2,800 Machine Learning engineers in Scalable Path’s network who are evaluated not just for their technical ability, but also their values, teamwork, problem solving, and key soft skills.
Calin
Machine Learning EngineerSoftware developer with a strong passion for Deep Learning and Data Science. Calin has worked in Machine Learning for 3+ years, while also acquiring a Master's Degree in Artificial Intelligence at the University of Bucharest. In previous projects, Calin was involved in understanding clients' needs, prototyping Machine Learning solutions, and developing them to be deployed on different devices from GPUs to mobile platforms.
Skills: Data Science, Machine Learning, Data Visualization, Data Analysis, HTML, TypeScript, Angular, SQL, MySQL, Apache Spark
Jean
Machine Learning EngineerJean is a software developer with 5+ years' experience working with libraries like Django, SciKit-learn, Django Rest Framework, SwampDragon. He is curious and self-motivated, and always looking for innovative machine learning and AI projects that allow him to continue growing his skills.
Skills: Linux, Git, Ubuntu, Bash, HTML, Python, Django, Manual QA, English, SQL
Agustin
Data Scientist and Machine Learning EngineerAgustín is a versatile data scientist and data engineer particularly interested in data analysis and artificial intelligence. For these reasons, he has focused his career on machine learning processes, especially DNNs. He is also a experienced Python developer.
Skills: English, Natural Language Processing, Machine Learning, Data Science, MongoDB, Redis, Linux, Flask, Apache Spark, Kubernetes
Nicolas
Data Scientist and Machine Learning EngineerNicolas is a data scientist with 5+ years' experience exploring, preparing, modeling, and evaluating data. He's also skilled in analyzing/modeling experiments (such as A/B test and multi-armed bandit). Nicolas has a solid background working with classification models and recommendation systems, leading a team, and managing product roadmaps.
Skills: Data Analysis, English, Data Visualization, Python, Machine Learning, Data Science, Bash, Natural Language Processing, Git, Spark
Jonathan
Machine Learning EngineerData Scientist and Quantitative Analyst certificated on Algorithmic Trading. Passionate about applying Data Science, Big Data, Programming, and Math to solve real-life problems. Python and R programmer. Enthusiastic Machine Learning and Artificial Intelligence apprentice.
Skills: Data Science, Data Analysis, Statistics, Data Visualization, Python, Google Cloud, MSSQL, D3.js, Flask, MySQL Management
After we’ve completed our rigorous vetting, you have the opportunity to meet the best-suited candidates in a 1:1 video call. Review in-depth candidate notes, schedule interviews with your favorites, and make your pick. You’re in full control.
After recently using other platforms, I’m impressed with the transparency & thoroughness of Scalable Path, as well as how aligned the candidates they’ve sent over are with our criteria.
Head of Engineering, Fiveable
Finding the right fit for my AI startup was tough—I needed someone with passion and skill. Scalable Path delivered fast. We couldn't have hit our goals without them.
Founder & CEO, Smarty
The team Scalable Path assembled for us crushes the mark on every critical point. The quality, speed and cost are impressive, but it’s the excellent customer experience and trust we’ve developed that set them apart.
SVP Products, Prolucent Health
Answers to questions about how Scalable Path engages with clients.
At Scalable Path, every Machine Learning engineer is carefully vetted to ensure they are among the best in their field. Our thorough screening process, including application questions, screening interview and technical exam, validates the skills of each applicant and ensures that we find the best candidates for your business needs.
Although we have Machine Learning engineers from all over the world, we commonly work with Machine Learning engineers in regions like Latin America and Eastern Europe. This keeps our rates low while ensuring teams and clients are in overlapping time zones, which is optimal for collaboration. Rates vary per developer but typically range between $50-$75 USD/hour.
Each applicant goes through an initial screening interview where we evaluate the candidate's experience, personality, availability, and English communication skills. If the candidate passes, our technical experts conduct a 1-hour technical screen-sharing interview where they complete a real-world coding exercise relevant to the project they are applying for. Only the best candidates get presented to our clients, so you can feel confident that each Machine Learning engineer you work with will communicate well and excel in their field.
We record all of our technical interviews with our developers and can share access with our clients. We offer this so that clients can gain a better understanding of how the candidate performed during the technical assessment, how they work, and what their strengths are before interviewing or hiring them.
We can generally present candidates to clients within a week. We take the time to thoroughly vet each candidate according to your unique project and business needs to ensure they are a great fit for your role. In some cases, if the stars align, we can present candidates in a day or two, but we prefer to underpromise and overdeliver.
Speak with a seasoned software architect from the start. Not a salesperson.
Machine learning (ML) has evolved from an experimental novelty to a fundamental part of the software landscape. As ML adoption has grown, development and deployment processes have had to adapt—and continue to evolve—to meet the demands of this field. ML models are built...
AI, like much of tech, evolves at an exponential pace. Just a few years ago, we saw an explosion of LLM-powered tools that streamlined development tasks. Innovations like ChatGPT, GitHub Copilot, Cursor, and Tabnine serve as both code auto-completion tools and AI pair programming...
Langchain has rapidly emerged as a game-changer in the development of AI-powered applications. As developers increasingly leverage large language models (LLMs), Langchain simplifies what was once a fragmented and complex process. By offering an open-source orchestration framework, Langchain allows developers to easily integrate LLMs into...
ChatGPT has quickly become a go-to tool in the world of AI since its launch. And it’s easy to see why: ChatGPT can generate cohesive, grammatically correct written content based on prompts, translate text, write code, and perform countless useful tasks for marketers, developers, and data analysts. Don’t feel like reading? We made...
Hello everyone! I’m back to talk about one of the most-discussed subjects in AI innovation recently: AI agentic workflows.
Artificial Intelligence has been shaking up nearly every industry, and software development is no exception. Companies are constantly looking to optimize their processes and reduce costs, and this is where the promise of integrating AI into the daily routine of software developers shines the brightest....
Hugging Face stands out as a transformative toolkit in the NLP ecosystem. In this article, we’ll dive into what sets Hugging Face apart, exploring its core features, the diverse tasks it supports, the vibrant community behind it, and a practical example showcasing its user-friendly interface.
In this article, I’ll walk you there my discoveries and revelations that unfolded during these weeks of coding with ChatGPT. First, we’ll dive deep into the various functions and capabilities I used, such as code analysis and suggestions, code refactoring, debugging assistance, documentation generation, and code reviewing. There, I’ll showcase real-life code examples from my TypeScript, GraphQL, NestJS, and MongoDB projects.
Since its release, ChatGPT has taken the world by storm. Seemingly out of nowhere, we had a computational system so effective that it could generate human language and dialogue good enough to pass expert evaluation tests like the bar or medical exams. One of the reasons ChatGPT became so popular is the simplicity of the user experience. It allows users to access the power of […]
As artificial intelligence, or AI, increasingly becomes a part of our everyday lives, the need for understanding the systems behind this technology as well as their failings, becomes equally important. It’s simply not acceptable to write AI off as a foolproof black box that outputs sage advice. In reality, AI can be as flawed as its creators, leading to negative outcomes in the real world
