Job description
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 http://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 http://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 (Py. Torch, Tensor. Flow).
- 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).