> Markdown version of [/jobs/ext/2702698-machine-learning-engineer](https://www.wearedevelopers.com/jobs/ext/2702698-machine-learning-engineer). Every page supports `.md` or `Accept: text/markdown`. Links point to the HTML versions so they work for humans too. Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md). --- # Machine Learning Engineer - **Company:** ML Inc. - **Location:** United States - **Contract:** Permanent contract - **Skills:** Artificial Intelligence, Airflow, Data Structures, Software Debugging, Software Design Patterns, Python (Programming Language), Linear Programming, Machine Learning, Language Modeling, Open Source Technology, Tensorflow, Data Processing, Pytorch, Large Language Models, Apache Spark, Deep Learning, Production Code, Machine Learning Operations, Multiaccess Edge Computing, Data Pipelines - **Published:** September 4, 2026 - **Apply:** https://startup.jobs/machine-learning-engineer-nace-ai-8304151 ## About the Role * 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., * 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). ## Description 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., * 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. ## Related Videos - [DevOps for AI: running LLMs in production with Kubernetes and KubeFlow](https://www.wearedevelopers.com/videos/1222-devops-for-ai-running-llms-in-production-with-kubernetes-and-kubeflow) - [Photonic Computing: Programming a New Class of AI Accelerators (incl. Live Coding)](https://www.wearedevelopers.com/videos/100196-photonic-computing-programming-a-new-class-of-ai-accelerators-incl-live-coding) - [From Messy Queries to Scalable Systems - How Data Engineering actually works](https://www.wearedevelopers.com/videos/100203-from-messy-queries-to-scalable-systems-how-data-engineering-actually-works) - [Machine learning in the browser with TensorFlowjs](https://www.wearedevelopers.com/videos/155-machine-learning-in-the-browser-with-tensorflowjs) - [AI Model Management Life Circles: ML Ops For Generative AI Models From Research to Deployment](https://www.wearedevelopers.com/videos/1152-ai-model-management-life-circles-ml-ops-for-generative-ai-models-from-research-to-deployment) - [Serverless deployment of (large) NLP models ](https://www.wearedevelopers.com/videos/158-serverless-deployment-of-large-nlp-models) ## Related Articles - [What Are Large Language Models?](https://www.wearedevelopers.com/magazine/304-what-are-large-language-models) - [How to Become an AI Engineer](https://www.wearedevelopers.com/magazine/331-how-to-become-an-ai-engineer) - [MLops – Deploying, Maintaining And Evolving Machine Learning Models in Production](https://www.wearedevelopers.com/magazine/115-mlops-deploying-maintaining-and-evolving-machine-learning-models-in-production) - [MLOps – What’s the deal behind it?](https://www.wearedevelopers.com/magazine/125-mlops-what-s-the-deal-behind-it) - [MLOps And AI Driven Development](https://www.wearedevelopers.com/magazine/82-mlops-and-ai-driven-development) - [The Best Large Language Models on The Market](https://www.wearedevelopers.com/magazine/319-the-best-large-language-models-on-the-market)