Job description template

Machine Learning Engineer Job Description

Recruit top Machine Learning Engineers with this comprehensive job description template. Covers ML systems, model deployment, technical skills, and salary.

The role in brief

A Machine Learning Engineer bridges the gap between data science research and production software systems by designing, building, and deploying machine learning models at scale. While Data Scientists focus on experimentation and model development, ML Engineers own the end-to-end lifecycle of taking models from prototype to production, ensuring they are reliable, performant, and maintainable. This role requires a rare combination of software engineering rigor, mathematical understanding, and MLOps expertise. ML Engineers work on recommendation systems, natural language processing, computer vision, fraud detection, and other AI-powered product features that directly impact users.

Design and implement production machine learning pipelines including feature engineering, model training, evaluation, and deployment workflows
Translate data science prototypes and research notebooks into robust, tested, and scalable production code
Build and maintain ML infrastructure including feature stores, model registries, experiment tracking systems, and automated retraining pipelines
Optimize model performance for latency, throughput, and cost by implementing techniques such as quantization, distillation, and efficient inference serving

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Responsibilities

Design and implement production machine learning pipelines including feature engineering, model training, evaluation, and deployment workflows
Translate data science prototypes and research notebooks into robust, tested, and scalable production code
Build and maintain ML infrastructure including feature stores, model registries, experiment tracking systems, and automated retraining pipelines
Optimize model performance for latency, throughput, and cost by implementing techniques such as quantization, distillation, and efficient inference serving
Implement monitoring and observability for deployed models including data drift detection, prediction quality tracking, and automated alerting
Collaborate with Data Scientists on model selection, hyperparameter tuning, and evaluation methodology to ensure models meet business requirements
Design A/B testing and experimentation frameworks to measure the real-world impact of ML features on product metrics
Develop APIs and microservices for real-time model inference using frameworks like FastAPI, TensorFlow Serving, or Triton Inference Server
Ensure responsible AI practices by implementing fairness checks, bias detection, and explainability tools in ML pipelines

Required skills

Python programming with deep expertise in ML libraries (scikit-learn, PyTorch, TensorFlow, XGBoost)ML system design and production deployment patternsMLOps tools and practices (MLflow, Kubeflow, Weights & Biases, DVC)Cloud ML services (AWS SageMaker, Google Vertex AI, Azure ML)Software engineering best practices (testing, CI/CD, version control, code review)Feature engineering and data preprocessing at scaleModel evaluation metrics and statistical analysisContainerization and orchestration (Docker, Kubernetes)SQL and data manipulation for feature pipeline development

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Nice to have

Deep learning architectures (transformers, CNNs, RNNs) for specialized applicationsLarge Language Model fine-tuning and prompt engineering (LLMs, RAG pipelines)Distributed training frameworks (Horovod, DeepSpeed, PyTorch Distributed)Edge deployment and model optimization (ONNX, TensorRT, CoreML)Experience with real-time streaming inference using Kafka or Flink

Qualifications

1Master's or Ph.D. in Computer Science, Machine Learning, Statistics, or related quantitative field preferred; Bachelor's with strong experience also considered
23-5 years of experience building and deploying machine learning systems in production environments
3Published work, open-source contributions, or a portfolio demonstrating ML engineering projects
4Strong understanding of linear algebra, probability, statistics, and optimization theory
5Experience with at least one end-to-end ML deployment from prototype to production serving millions of predictions

Compensation and environment

Salary range

$130,000 - $200,000 per year depending on experience and specialization. Senior and Staff ML Engineers at major tech companies earn $200,000 - $350,000+ including equity.

Work environment

Primarily remote or hybrid. ML Engineers typically work within cross-functional teams alongside Data Scientists, Software Engineers, and Product Managers. The role involves a mix of deep individual technical work and collaborative design sessions. Access to GPU clusters and cloud computing resources is standard.

Career growth

ML Engineers can advance to Senior ML Engineer, Staff ML Engineer, ML Platform Lead, or ML Engineering Manager. Specialized paths include Research Engineer, Applied Scientist, or AI Architect roles. Many progress to Director of AI/ML or CTO positions at AI-focused companies.

Common questions

What is the difference between a Machine Learning Engineer and a Data Scientist?

Data Scientists focus on exploratory analysis, statistical modeling, and developing ML models in research or notebook environments. ML Engineers specialize in taking those models and building production systems around them, including deployment infrastructure, monitoring, scaling, and maintenance. ML Engineers typically have stronger software engineering skills, while Data Scientists have deeper statistical expertise.

Do I need a Ph.D. to become a Machine Learning Engineer?

A Ph.D. is not strictly required for most ML Engineering positions, especially those focused on applying established techniques rather than developing novel algorithms. A master's degree with strong engineering experience or a bachelor's degree with significant practical ML experience can be sufficient. Portfolio projects demonstrating production ML systems are highly valued.

What is MLOps and why is it important for ML Engineers?

MLOps (Machine Learning Operations) encompasses the practices, tools, and culture for deploying and maintaining ML models in production reliably and efficiently. It includes experiment tracking, model versioning, automated retraining, monitoring for data drift, and CI/CD for ML pipelines. MLOps is critical because without it, ML models degrade over time and become unmaintainable.

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