Machine Learning Engineer

Nace.AI; Palo Alto (CA); On-site; USD 120,000 - 160,000; Full time

Job summary

A leading AI technology company is seeking a Machine Learning Engineer to translate cutting-edge research into scalable solutions. You will design and maintain ML systems, fine-tune large language models, and ensure robust integration into applications. The ideal candidate has experience in ML frameworks, strong programming skills in Python, and a solid educational background in Computer Science. The position is on-site in Palo Alto, CA, offering opportunities to work on high-impact features that drive the company's strategic objectives.

Qualifications

• Hands-on experience training and fine-tuning large language models.
• Proven experience scaling inference infrastructure.
• Ability to translate research into production-ready code.

Responsibilities

• Design, build, and maintain end-to-end ML systems.
• Fine-tune large language models and implement meta-learning methods.
• Incorporate advancements from recent ML research into existing models.

Skills

Training and fine-tuning large language models (LLMs)

Experience with Deep Learning Models

Proficient in Python

Solid foundation in computer science fundamentals

Experience with ML frameworks and libraries

Education

BS degree in Computer Science or related field

Tools

PyTorch

TensorFlow

Job description

Location

Palo Alto, CA

Employment Type

Full time

Location Type

On-site

Department

Engineering

Role Overview

As a Machine Learning Engineer, you will play a central role in translating cutting-edge machine learning research into scalable, production-ready solutions. You will collaborate closely with cross-functional teams to identify opportunities where ML can drive product value, architect robust model-centric systems, and ensure their seamless integration into real-world applications. The role requires a strong balance between theoretical understanding and engineering execution, with a focus on building reliable, maintainable, and high-impact AI-driven features that align with Nace.AI’s strategic objectives.

Key Responsibilities

• Design, build, and maintain end-to-end ML systems, including synthetic data pipelines, model training, debugging, and performance evaluation.
• Fine-tune large language models (LLMs) and implement meta-learning methods to enhance model generalization and efficiency.
• Improve existing Nace.AI models by incorporating advancements from recent ML research.

Qualifications

• Hands-on experience training and fine-tuning large language models (LLMs) and vision-language models (VLMs), including practical work with pre-training, instruction tuning, and alignment techniques (GRPO, RLHF/DPO/PPO).
• Hands-on Experience with Deep Learning Models, especially Transformers.
• Ability to translate cutting-edge research from papers into clean, production-ready code (Paper to Code).
• Proven experience scaling inference infrastructure for LLMs/VLMs, including expertise in model serving frameworks like vLLM, TGI.
• Proficient in Python with a strong track record of building substantial projects.
• Solid foundation in computer science fundamentals (data structures, algorithms, design patterns).
• BS degree in CS or related technical field.
• Solid Experience with ML frameworks and libraries (PyTorch, TensorFlow).
• Self-starter comfortable working in a fast-paced, dynamic environment.

Preferred Qualifications

• MS/PhD in CS or related technical field.
• Familiarity with data processing stacks such as Spark and Airflow.
• Experience with multi-node GPU training.
• Contributor to open-source ML projects.
• Deep knowledge in Linear Programming.
• Experience with advanced NLP and Multimodal post-training experience (e.g., model distillation, quantization, deployment optimization).
• Experienced in inference time optimization, deep understanding of LLM serving optimizations for LLMs/VLMs.
• Hands on experience with quantization techniques (AWQ, GPTQ, FP8/GGUF).