Machine Learning Engineer

Also known as: AI Engineer, Deep Learning Engineer, MLOps Engineer, AI Solutions Architect

See 47 live Machine Learning Engineer jobs

Role Overview

The Machine Learning Engineer (AI Foundry) is a pivotal role at the forefront of artificial intelligence innovation. This position involves designing, developing, and deploying sophisticated machine learning models and AI systems that drive transformative solutions. AI Foundries are specialized environments, often within larger organizations or dedicated research labs, focused on rapid prototyping, experimentation, and scaling of cutting-edge AI technologies. Engineers in this setting are tasked with translating complex business problems into actionable AI strategies, building robust pipelines, and ensuring the ethical and efficient integration of AI into products and services.

The demand for skilled Machine Learning Engineers, particularly those with experience in specialized AI Foundry environments, is experiencing explosive growth. As businesses increasingly recognize the competitive advantage offered by AI, the need for professionals who can bridge the gap between theoretical research and practical application has never been higher. This role is crucial for organizations looking to harness the power of AI to automate processes, gain deeper insights, predict trends, and create entirely new user experiences. The ability to work with large datasets, understand complex algorithms, and implement production-ready AI solutions makes this a highly sought-after and impactful career path.

Key Responsibilities

  • Design, develop, and implement scalable machine learning models and AI algorithms tailored to specific business needs within the AI Foundry.
  • Build and maintain robust data pipelines for training, validation, and deployment of ML models, ensuring data quality and integrity.
  • Collaborate with data scientists, researchers, and product managers to translate research prototypes into production-ready AI solutions.
  • Optimize ML models for performance, efficiency, and scalability across various hardware and software environments.
  • Develop and implement MLOps practices for continuous integration, continuous delivery, and continuous monitoring of AI models.
  • Evaluate and select appropriate ML frameworks, libraries, and tools for specific project requirements.
  • Conduct rigorous testing and validation of AI models to ensure accuracy, reliability, and fairness.
  • Stay abreast of the latest advancements in ML, deep learning, and AI research, and apply them to innovative projects.
  • Troubleshoot and debug complex ML systems, identifying and resolving issues in production environments.
  • Contribute to the ethical considerations and responsible deployment of AI technologies, ensuring compliance with relevant guidelines.
  • Document code, models, and processes thoroughly for knowledge sharing and future reference.
  • Participate in code reviews and provide constructive feedback to team members.

Required Skills

Technical Skills

Proficiency in Python and its core ML libraries (TensorFlow, PyTorch, Scikit-learn) Strong understanding of statistical modeling and data analysis techniques Experience with various machine learning algorithms (supervised, unsupervised, reinforcement learning) Knowledge of deep learning architectures (CNNs, RNNs, Transformers) Experience with data preprocessing, feature engineering, and model evaluation Familiarity with MLOps principles and tools (e.g., Docker, Kubernetes, CI/CD pipelines) Experience with cloud platforms (AWS, Azure, GCP) and their ML services Database management and SQL proficiency Understanding of distributed computing frameworks (e.g., Spark) Version control systems (Git)

Soft Skills

Problem-solving and analytical thinking Strong communication and collaboration skills Adaptability and willingness to learn new technologies Attention to detail and commitment to quality Ability to work independently and as part of a team Proactive and results-oriented

Tools & Technologies

TensorFlow PyTorch Scikit-learn Docker Kubernetes Git Jupyter Notebooks Cloud ML Platforms (e.g., AWS SageMaker, Azure ML, Google AI Platform)

Seniority Levels

A Junior Machine Learning Engineer in an AI Foundry typically possesses 1-3 years of experience. Their primary focus is on assisting senior engineers and data scientists in developing and implementing ML models. This includes tasks such as data cleaning and preprocessing, running experiments, and contributing to the codebase under supervision. They are expected to have a solid foundational understanding of core ML concepts and programming languages like Python.

Key responsibilities at this level often involve implementing existing algorithms, writing scripts for data manipulation, and performing basic model evaluations. Junior engineers are encouraged to learn and grow within the team, gaining hands-on experience with MLOps tools and cloud infrastructure. They will be involved in debugging code, documenting findings, and participating in team discussions. Salary expectations for a Junior Machine Learning Engineer can range from $70,000 to $95,000 annually, depending on the specific location and company.

Frequently Asked Questions

What is an 'AI Foundry' in the context of this role?
An AI Foundry is a specialized environment, often within a company or research institution, dedicated to the rapid development, experimentation, and scaling of artificial intelligence technologies. It's a hub for innovation where machine learning engineers, data scientists, and researchers collaborate to build and deploy cutting-edge AI solutions.
What is the difference between a Machine Learning Engineer and a Data Scientist?
While there's overlap, Data Scientists typically focus more on analyzing data, identifying patterns, and building statistical models for insights. Machine Learning Engineers are primarily concerned with taking those models and making them production-ready, scalable, and efficient for real-world applications. They build the pipelines and infrastructure to deploy and maintain ML systems.
What are the most in-demand skills for an AI Foundry ML Engineer?
Key in-demand skills include deep proficiency in Python and its ML libraries (TensorFlow, PyTorch), strong understanding of various ML algorithms and deep learning architectures, experience with MLOps practices and tools, cloud platform expertise (AWS, Azure, GCP), and robust problem-solving abilities.
Is this role suitable for someone transitioning from software engineering?
Yes, absolutely. A strong background in software engineering is highly valuable for an ML Engineer. Skills in programming, system design, and deployment are transferable, and with focused learning in ML algorithms and data science principles, a software engineer can transition effectively.
What kind of projects can I expect to work on in an AI Foundry?
Projects can be diverse, ranging from developing natural language processing models for chatbots, creating computer vision systems for image recognition, building recommendation engines, to optimizing complex operational processes using reinforcement learning. The focus is on pushing the boundaries of what AI can achieve.
What is MLOps and why is it important for this role?
MLOps (Machine Learning Operations) is a set of practices that combines Machine Learning, DevOps, and Data Engineering to manage the end-to-end ML lifecycle. It's crucial for ensuring that ML models can be reliably deployed, monitored, and updated in production environments, making the AI Foundry's work sustainable and impactful.

Salary Range

$70k - $150k /year

Based on global market data. Salaries vary significantly by location, experience, and company size.

Career Path

1
Lead Machine Learning Engineer
2
AI Architect
3
Director of AI/ML
4
Principal Data Scientist

Ready to apply?

We have 47 Machine Learning Engineer positions open right now.

Find Machine Learning Engineer Jobs

Related Roles

Data Scientist