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Key Responsibilities and Required Skills for a Training Engineer

💰 $130,000 - $225,000

EngineeringMachine LearningArtificial IntelligenceData Science

🎯 Role Definition

As a Training Engineer, you are the architect behind our machine learning model's intelligence. You will own the end-to-end process of training, evaluating, and optimizing cutting-edge models, from large language models (LLMs) to specialized computer vision systems. You'll work at the intersection of research and production, ensuring our models are not only state-of-the-art but also robust, scalable, and efficient. Your work will directly impact product features and drive the core capabilities of our platform.

📈 Career Progression

Typical Career Path

Entry Point From:

  • Machine Learning Engineer
  • Data Scientist
  • Research Engineer
  • Software Engineer (with a focus on ML/AI)

Advancement To:

  • Senior / Staff Training Engineer
  • MLOps Lead / Manager
  • Research Scientist
  • Engineering Manager (ML)

Lateral Moves:

  • MLOps Engineer
  • Data Engineer (ML-focused)
  • Applied Scientist

Core Responsibilities

Primary Functions

  • Design, build, and maintain scalable, end-to-end data pipelines for collecting, cleaning, and preprocessing massive datasets for model training.
  • Develop and implement robust training frameworks and infrastructure to support experimentation with state-of-the-art machine learning models, including LLMs and diffusion models.
  • Execute and manage large-scale distributed training jobs on GPU/TPU clusters, meticulously monitoring for performance, stability, and convergence.
  • Conduct systematic hyperparameter tuning and architecture search experiments to optimize model performance and achieve state-of-the-art results on key benchmarks.
  • Collaborate closely with research scientists to translate novel research ideas and algorithmic improvements into production-quality training code and workflows.
  • Implement and refine model evaluation strategies, developing novel metrics and comprehensive test suites to rigorously assess model quality, fairness, and safety.
  • Profile and optimize model performance, including memory usage, computational efficiency, and inference latency, using techniques like quantization, pruning, and distillation.
  • Develop and maintain internal tooling for experiment tracking, data versioning, model management, and results visualization to improve the team's research and development velocity.
  • Stay at the forefront of the latest advancements in deep learning, including new model architectures, training techniques, and hardware accelerators, and champion their adoption.
  • Author and maintain high-quality technical documentation for data processing, training procedures, and model specifications.
  • Own the full lifecycle of a model from initial data sourcing and prototyping to a production-ready, optimized artifact.
  • Debug complex issues in the ML training stack, spanning data quality, framework bugs, hardware failures, and network bottlenecks.
  • Create high-quality, large-scale training datasets through sophisticated data mining, filtering, and augmentation techniques.
  • Implement data-centric AI approaches, continuously analyzing and improving datasets to enhance model performance and robustness.
  • Manage and optimize cloud computing resources (e.g., AWS, GCP, Azure) to ensure cost-effective and timely execution of training and evaluation workloads.
  • Build frameworks for continuous model evaluation to monitor for performance degradation, concept drift, and data drift over time.
  • Partner with MLOps engineers to integrate training and evaluation pipelines into the broader CI/CD/CT (Continuous Training) ecosystem.
  • Analyze and troubleshoot model failures, performing deep dives into specific examples to understand root causes and propose mitigation strategies.
  • Design and conduct ablation studies to understand the impact of different data sources, model components, and training parameters.
  • Ensure the reproducibility of all experiments and training runs through meticulous configuration management and artifact tracking.
  • Develop custom data loaders and preprocessing steps to handle unique or challenging data modalities and formats.
  • Fine-tune pre-trained foundation models on domain-specific data to create specialized, high-performing models for various business applications.

Secondary Functions

  • Support ad-hoc data requests and exploratory data analysis to inform new research directions.
  • Contribute to the organization's data strategy and roadmap by identifying new and valuable data sources.
  • Collaborate with business units to translate data needs and product requirements into engineering specifications.
  • Participate in sprint planning, retrospectives, and other agile ceremonies within the machine learning team.

Required Skills & Competencies

Hard Skills (Technical)

  • Expert-level proficiency in Python and extensive experience with core data science libraries such as Pandas, NumPy, and Scikit-learn.
  • Deep, hands-on experience with modern ML frameworks, such as PyTorch (preferred), TensorFlow, or JAX, including writing custom modules and optimizers.
  • Proven experience in training large-scale deep learning models, particularly transformers, CNNs, or GNNs, in a distributed environment (DDP, FSDP, DeepSpeed).
  • Strong understanding of cloud computing platforms (AWS, GCP, or Azure) and their associated ML services (e.g., SageMaker, Vertex AI, Azure ML).
  • Proficiency with containerization and orchestration technologies like Docker and Kubernetes for creating reproducible and scalable ML environments.
  • Experience with MLOps tooling for experiment tracking (e.g., Weights & Biases, MLflow), data versioning (DVC), and workflow automation (e.g., Kubeflow, Airflow).
  • Solid software engineering fundamentals, including knowledge of data structures, algorithms, and best practices for writing clean, testable, and maintainable code.
  • Familiarity with data processing at scale using tools like Spark, Dask, or Ray.
  • Knowledge of SQL and NoSQL databases for querying and managing structured and unstructured data.
  • Understanding of model optimization techniques for efficient inference, such as quantization, pruning, and knowledge distillation.

Soft Skills

  • Exceptional analytical and problem-solving skills, with a proven ability to debug complex systems and tackle ambiguous challenges.
  • Strong communication and collaboration abilities, capable of effectively conveying complex technical concepts to both technical and non-technical stakeholders.
  • A pragmatic, results-oriented mindset with a strong sense of ownership and the ability to drive projects from conception to completion.
  • High attention to detail and a commitment to scientific rigor and reproducibility in experimentation.
  • Inherent curiosity and a passion for continuous learning to stay on top of the rapidly evolving AI/ML landscape.
  • Adaptability and resilience to navigate the iterative and often uncertain nature of research and development.

Education & Experience

Educational Background

Minimum Education:

  • Bachelor's Degree in a quantitative or computational field.

Preferred Education:

  • Master's Degree or Ph.D. with a focus on Machine Learning, AI, or a related discipline.

Relevant Fields of Study:

  • Computer Science
  • Machine Learning
  • Artificial Intelligence
  • Statistics
  • Physics
  • Mathematics

Experience Requirements

Typical Experience Range:

  • 3-7+ years of professional experience in a machine learning, data science, or software engineering role.

Preferred:

  • Demonstrated experience in training and deploying large-scale deep learning models in a production or advanced research environment. A portfolio of projects, publications, or contributions to open-source ML frameworks is highly desirable.
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