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).