Senior Machine Learning Engineer

Team: Software Development

Location: Lehi, Utah

Commitment: Full-Time

Workplace Type: hybrid

Responsibilities

Fine-tune, adapt, and align open-weights foundation models for domain-specific property management use cases using Supervised Fine-Tuning (SFT), preference optimization (DPO/RLHF), and parameter-efficient methods (LoRA/QLoRA).

Build scalable data filtering, instruction-tuning curation, and synthetic data generation pipelines tailored to prepare high-quality datasets for open-model training and continuous alignment.

Optimize open-model inference, serving, quantization, and deployment using frameworks like vLLM, TensorRT-LLM, or SGLang to maximize GPU throughput, lower latency, and minimize operating costs for FORGE.

Develop agentic AI systems leveraging function-calling, reasoning, and context window capabilities to automate multi-step enterprise workflows across Entrata’s platform.

Build domain-specific evaluation frameworks and safety guardrails to measure task accuracy, hallucination rates, and reliability of fine-tuned models against proprietary benchmarks.

Explore and deploy specialized model variants to expand FORGE’s multimodal and automated workflows.

Partner with product, data, and software engineering teams to integrate self-hosted open models seamlessly into Entrata’s microservices and production APIs.

Establish best practices for model versioning, open-model artifact management, continuous evaluation, and post-deployment monitoring.

Minimum Qualifications

• 5+ years of software engineering or machine learning engineering experience.
• Hands-on experience fine-tuning, adapting, or deploying large language models, including parameter-efficient techniques such as LoRA and QoRA (QLoRA).
• 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.
• Willingness to travel to client sites as needed.

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.