Machine Learning Engineer

About Us: At Nace AI, we are redefining how professional services operate by delivering Sovereign Specialized Intelligence. As an applied research and product company, we equip enterprises with a comprehensive AI stack to build customized, secure intelligence tailored to their unique business needs. Driven by advanced Small Language Models and our dynamic metamodel framework, our flagship platforms

• Nace Data Intelligence and the Nace SLM Cloud
• enable true end-to-end business process automation. The result is transformative ROI: professional services firms using Nace AI are currently recovering 1,000 hours per client engagement, drastically reducing overhead and accelerating delivery. The work we are doing has a meaningful impact across industries, and every hire at Nace AI plays a critical role in shaping the company’s trajectory. This is a unique opportunity to join a high conviction AI company at an early stage and directly influence its growth. If building a world-class AI team from the ground up excites you, we’d love to talk. 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:
• 3+ years of hands-on experience building and deploying machine learning systems in production environments.
• 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:
• 5+ years of industry experience in machine learning engineering, with a track record of shipping LLM-based systems at scale.
• 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).