What Does a Machine Learning Engineer Do?
A machine learning engineer designs, builds, and deploys artificial intelligence systems that can learn from data and improve over time. These professionals sit at the intersection of software engineering and data science, applying advanced algorithms and statistical models to create intelligent applications. Their work powers everything from recommendation engines and fraud detection tools to autonomous systems and predictive analytics.
Machine learning engineers are responsible for developing scalable ML pipelines, tuning model performance, and integrating machine learning solutions into production environments. They work closely with data scientists to transform prototypes into deployable code and often collaborate with data engineers, DevOps, and product teams. Mastery of frameworks like TensorFlow or PyTorch, along with strong programming and cloud infrastructure skills, is essential.
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Machine Learning Engineer Core Responsibilities
- Design, develop, and deploy machine learning models for real-world applications
- Collaborate with data scientists to turn research prototypes into production-ready solutions
- Build and maintain scalable ML pipelines for training and inference
- Optimize model performance, accuracy, and efficiency
- Implement monitoring, logging, and retraining strategies (MLOps)
- Handle data preprocessing, feature engineering, and labeling workflows
- Integrate models with applications via RESTful APIs or other deployment methods
- Stay current with advancements in machine learning, deep learning, and AI tooling
Required Skills and Qualifications
Hard skills
- Proficiency in Python and ML libraries (TensorFlow, PyTorch, Scikit-learn)
- Experience with cloud platforms (AWS SageMaker, GCP Vertex AI, Azure ML)
- Familiarity with MLOps tools (MLflow, Kubeflow, Airflow, Docker, Kubernetes)
- Strong understanding of supervised, unsupervised, and deep learning techniques
- Experience with model versioning, deployment, and performance monitoring
- Knowledge of distributed systems and scalable data pipelines
Soft skills
- Strong problem-solving and debugging skills
- Clear communication with cross-functional stakeholders
- Attention to detail in model validation and experimentation
- Adaptability in fast-changing technical environments
Education
- Bachelor’s degree in computer science, software engineering, data science, or mathematics required
- Master’s degree preferred for roles involving complex model development or research
Certifications
- Not required
- Preferred: Google Cloud ML Engineer, AWS Machine Learning Specialty, or Deep Learning Specialization (Coursera/Stanford/Deeplearning.ai)
Preferred Qualifications
- 2–4 years of experience in ML engineering, data science, or AI product development
- Experience deploying models to production environments with continuous retraining
- Familiarity with reinforcement learning, NLP, or computer vision (depending on use case)
National Average Salary
ML engineer salaries vary by experience, industry, organization size, and geography. Click below to explore salaries by local market.
The average national salary for a Machine Learning Engineer is:
$122,375
Sample Job Description Templates for Machine Learning Engineers
Natural Language Processing Engineer
Position Overview
We’re seeking an NLP Engineer to build and optimize language-based machine learning models. This role involves designing algorithms that enable machines to understand, interpret, and generate human language across use cases like chatbots, text classification, and document processing.
Responsibilities
- Build, train, and fine-tune NLP models using deep learning and transformer architectures
- Preprocess and tokenize large-scale text datasets for model consumption
- Implement named entity recognition (NER), sentiment analysis, and intent detection pipelines
- Optimize model accuracy, latency, and generalizability
- Collaborate with product, data, and engineering teams to integrate NLP features into applications
Requirements
Hard skills
- Proficiency in Python, Hugging Face Transformers, spaCy, NLTK, or similar libraries
- Strong understanding of LLMs, BERT, GPT, or other pre-trained NLP models
- Experience with text preprocessing, embeddings, and vector similarity techniques
Soft skills
- Curiosity about linguistics and language structure
- Ability to translate business problems into NLP solutions
- Clear documentation and cross-team communication
Education
- Bachelor’s degree in computer science, computational linguistics, or AI/ML
- Master’s preferred for advanced NLP modeling
Preferred Qualifications
- 2+ years working on NLP-focused applications or research
Computer Vision Engineer
Position Overview
We are hiring a Computer Vision Engineer to develop ML systems that process and interpret visual data. You’ll work on solutions involving image classification, object detection, OCR, and video analysis for real-world production environments.
Responsibilities
- Design and deploy computer vision models for image and video tasks
- Develop custom architectures using CNNs, YOLO, or EfficientNet
- Annotate and manage large-scale visual datasets
- Optimize models for inference speed and hardware limitations
- Collaborate with hardware, robotics, or software teams to integrate models
Requirements
Hard skills
- Proficiency with OpenCV, PyTorch/TensorFlow, and vision libraries (e.g., Detectron2)
- Experience with image segmentation, object tracking, and model compression
- Familiarity with edge deployment or GPU inference
Soft skills
- Creative problem-solving using visual data
- Precision in annotation and data quality control
- Ability to troubleshoot complex model behaviors in production
Education
- Bachelor’s degree in computer vision, computer science, or a related field
Preferred Qualifications
- 2–4 years building and deploying CV models in industry settings
MLOps Engineer
Position Overview
We are hiring an MLOps Engineer to design and maintain the infrastructure that supports scalable, automated ML workflows. This role bridges data science and DevOps by enabling continuous integration, deployment, and monitoring of ML models.
Responsibilities
- Build and maintain ML pipelines for data processing, training, and deployment
- Develop CI/CD systems for ML model versioning and reproducibility
- Implement model monitoring, logging, and alerting frameworks
- Collaborate with data scientists to streamline experimentation
- Optimize infrastructure for cost, speed, and compliance
Requirements
Hard skills
- Expertise in tools like MLflow, Kubeflow, Airflow, Docker, and Kubernetes
- Experience with cloud platforms (AWS SageMaker, GCP Vertex AI, Azure ML)
- Familiarity with CI/CD, IaC (Terraform), and monitoring tools (Prometheus, Grafana)
Soft skills
- Systems thinking and performance tuning mindset
- Strong collaboration with engineering and data teams
- Adaptability in evolving DevOps and MLOps practices
Education
- Bachelor’s degree in computer science or software engineering
Preferred Qualifications
- 2+ years of DevOps or ML infrastructure experience in production environments
Deep Learning Engineer
Position Overview
We’re seeking a Deep Learning Engineer to design, train, and deploy advanced neural networks across high-impact use cases. This role will work on state-of-the-art architectures in vision, NLP, or generative AI.
Responsibilities
- Develop and fine-tune deep learning models using TensorFlow or PyTorch
- Explore architecture choices such as CNNs, RNNs, GANs, or Transformers
- Manage training at scale on GPU clusters
- Conduct hyperparameter optimization and model evaluation
- Stay informed on the latest research in deep learning and AI
Requirements
Hard skills
- Strong coding skills in Python and deep learning frameworks
- Experience with high-dimensional data and neural network optimization
- Familiarity with distributed training and model parallelism
Soft skills
- Research-driven approach to experimentation
- Attention to reproducibility and model documentation
- Strong math/stats foundation for understanding model behavior
Education
- Bachelor’s in computer science or math; master’s or PhD strongly preferred
Preferred Qualifications
- Published work or portfolio of deep learning projects
Edge AI Engineer
Position Overview
We are hiring an Edge AI Engineer to design and deploy machine learning models on edge devices with limited compute capacity. You’ll work closely with hardware and embedded systems teams to create low-latency, high-efficiency AI solutions.
Responsibilities
- Optimize and compress ML models for deployment on mobile or IoT devices
- Work with platforms like TensorFlow Lite, ONNX, and NVIDIA Jetson
- Collaborate with hardware teams to ensure compatibility and performance
- Implement energy-efficient inference and model quantization techniques
- Monitor deployed models for latency, accuracy, and drift
Requirements
Hard skills
- Experience with edge inference libraries and embedded ML tooling
- Strong understanding of model pruning, quantization, and conversion
- Familiarity with ARM architecture, GPUs, or ASICs
Soft skills
- Resource-conscious engineering mindset
- Collaboration with firmware, hardware, and QA teams
- Ability to prototype quickly and validate performance
Education
- Bachelor’s degree in electrical engineering, robotics, or embedded systems
Preferred Qualifications
- 2+ years working on real-time ML systems or AI at the edge