Senior Machine Learning Engineer

We are seeking a Senior Machine Learning Engineer to help build and scale Entrata’s applied AI capabilities. This role will focus on adapting and fine-tuning foundation models for property management use cases, building reliable model training and evaluation pipelines, and deploying AI systems into production.

Responsibilities

• Fine-tune and adapt large language models for Entrata-specific use cases using supervised fine-tuning and other post-training techniques.
• Build scalable data preparation, curation, filtering, and synthetic data pipelines to support model training and evaluation.
• Develop agentic AI systems that can reason across multi-step workflows, use tools, retrieve context, and operate reliably in production.
• Build evaluation frameworks and benchmarks to measure model quality, safety, reliability, and task performance.
• Optimize model inference, serving, and deployment for performance, cost, and scalability.
• Partner with engineering, product, and data teams to integrate AI capabilities into Entrata products and workflows.
• Help establish best practices for model experimentation, fine-tuning, evaluation, and deployment.

Minimum Qualifications

• 5+ years of software engineering or machine learning engineering experience.
• Hands-on experience fine-tuning, adapting, or deploying large language models.
• Strong proficiency with Python and PyTorch or similar deep learning frameworks.
• Experience building ML data pipelines, training workflows, and evaluation systems.
• Experience deploying machine learning models into production environments.
• Familiarity with modern LLM tooling, model serving, and inference frameworks.
• Strong understanding of machine learning fundamentals and model performance tradeoffs.

Preferred Qualifications

• Experience with supervised fine-tuning, preference optimization, or related post-training techniques.
• Experience building agentic systems, tool-using models, or retrieval-based AI applications.
• Experience with distributed training or GPU-based model workloads.
• Familiarity with frameworks such as vLLM, DeepSpeed, FSDP, or similar technologies.
• Experience working with enterprise or domain-specific AI applications.
• Bachelor’s or advanced degree in Computer Science, Machine Learning, Engineering, or a related field, or equivalent practical experience.