Machine Learning Engineer, Applied

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Company Overview

A fast-growing artificial intelligence company is developing ambitious AI products designed to deliver reliable verification and quality systems. The organization operates in a high-performance environment where engineering teams work directly on advanced machine learning applications and production-ready AI technologies.

The role focuses on traditional machine learning, including training, fine-tuning, and evaluating models rather than solely building applications on top of existing APIs. The successful candidate will take ownership of datasets, training pipelines, applied models, and the complete model lifecycle supporting verification and quality features.

Compensation

Competitive salary plus meaningful equity.

About the Role

The organization is seeking a Machine Learning Engineer, Applied to develop and deploy machine learning models that power production AI products. The role requires hands-on experience with model training, fine-tuning, evaluation, datasets, and machine learning infrastructure.

The successful candidate will be rigorous about model evaluation, comfortable owning the complete model lifecycle, and capable of making practical decisions about when to train a model, fine-tune an existing model, or use an external API.

Key Responsibilities

• Train, fine-tune, and evaluate machine learning models for production use.
• Build and maintain datasets, labeling pipelines, and data quality checks.
• Own training infrastructure and experiment tracking.
• Develop applied models that power verification and quality-focused product features.
• Establish rigorous model evaluation methodologies and regression testing processes.
• Determine when to train models, when to fine-tune existing models, and when to leverage APIs.
• Collaborate with AI and product engineering teams to integrate models into production features.
• Own the model lifecycle from data preparation and experimentation through production deployment and monitoring.
• Maintain reliable and scalable machine learning workflows.
• Contribute to the continuous improvement of model performance, quality, and production reliability.

Required Qualifications

• Hands-on experience training and fine-tuning machine learning models.
• Strong understanding of machine learning fundamentals and evaluation methodologies.
• Experience with Python and modern machine learning technologies.
• Experience building datasets and data pipelines.
• Strong software engineering discipline and development practices.
• Clear written and verbal communication skills.
• Ability to operate effectively in a fast-moving, high-performance environment.
• Demonstrated experience shipping machine learning models to production or completing rigorous applied machine learning work.

Preferred Qualifications

• Experience fine-tuning large language models (LLMs) or working with embeddings at scale.
• Experience with PyTorch, JAX, or similar machine learning frameworks.
• Experience with model serving and inference optimization.
• Published machine learning research or technical work.
• Strong results in machine learning competitions.
• Significant open-source contributions related to machine learning or artificial intelligence.

Disclaimer

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