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Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md). --- # Senior/Lead Data Scientist (Applied AI Research) - **Company:** The Line - **Location:** United States (Remote available) - **Experience:** Expert - **Contract:** Permanent contract - **Skills:** Artificial Intelligence, Machine Learning, Open Source Technology, Pinecone, QLoRA, Qdrant, Open-source Models, Large Language Models, Multi-Agent Systems, Information Technology, Low Latency, Data Analytics, Milvus - **Published:** October 3, 2026 - **Apply:** https://arc.dev/remote-jobs/j/redirect/pou7sov4rn ## About the Role * Bachelor's, Master's, or Ph.D. in Computer Science, Data Analytics, Statistics, or a related quantitative field. * 2-5 years of experience in data science, machine learning, or applied research. * Solid foundation in statistics, probability, machine learning algorithms, and large language models (LLMs). * Hands-on experience with some modern AI tooling and frameworks (e.g., vLLM, LangChain, or similar execution engines). * Strong problem-solving skills, strong sense of ownership, and a passion for translating experimental research into production-grade systems. * Excellent communication skills with both technical and non-technical stakeholders, with a proven ability to build and foster collaborative relationships. * Ability to thrive in a dynamic, fast-paced environment and apply disciplined risk control to deliver high standards under constraints. * Fluency in both English and Thai. It'd be Great if you have: (if any) * Demonstrated experience deploying and optimizing LLM serving architectures for high-concurrency, low-latency production applications. * Extensive hands-on experience training, post-training, fine-tuning (e.g., LoRA, QLoRA), and aligning (e.g., RLHF, DPO) large language or multimodal models. * Familiarity with modern Retrieval-Augmented Generation (RAG) frameworks, vector databases (e.g., Pinecone, Milvus, Qdrant), and advanced retrieval techniques. * Experience building agentic workflows, function calling pipelines, or multi-agent orchestration systems. * Proven track record of publishing research papers at top-tier AI/ML conferences or journals. ## Description We are currently seeking a Data Scientist (Applied AI Research) to join our team. In this pivotal role, you will bridge the gap between emerging AI techniques and scalable production solutions. You will explore cutting-edge open-source models, experiment with fine-tuning techniques, and build harnesses while maintaining a grounded focus on business impact. This role requires an inquisitive research mindset paired with strong AI and software engineering fundamentals. Join us, and you'll work alongside talented, driven people who take real ownership from day one. Here, your work won't just move a project forward, it'll shape how millions of people live every day. What you'll Do: * Collaborate closely with engineering teams to design and deploy end-to-end LLM applications that drive measurable business impact. * Implement scalable LLMOps workflows to streamline model training, deployment, monitoring, and ongoing optimization in production. * Evaluate, test, and benchmark frontier and open-source LLMs to identify high-value applications across our product ecosystem. * Adapt and fine-tune pre-trained models using domain-specific datasets and modern optimization techniques. * Establish robust evaluation frameworks, testing harnesses, and monitoring infrastructure to ensure AI models optimize for accuracy, latency, safety, and cost efficiency. * Stay at the forefront of AI research, continuously evaluating emerging tools, architectures, and methodologies to enhance engineering practices. ## 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) - [What comes after ChatGPT? Vector Databases - the Simple and powerful future of ML?](https://www.wearedevelopers.com/videos/830-what-comes-after-chatgpt-vector-databases-the-simple-and-powerful-future-of-ml) - [The Retrieval Layer for Edge AI](https://www.wearedevelopers.com/videos/100033-the-retrieval-layer-for-edge-ai) - [Unboxing the DeepFace](https://www.wearedevelopers.com/videos/335-unboxing-the-deepface) - [AI Vector Search at Scale - Ewa Szyszka - Ewa Szyszka](https://www.wearedevelopers.com/videos/2161-ai-vector-search-at-scale-ewa-szyszka-ewa-szyszka) - [Self-Hosted LLMs: From Zero to Inference](https://www.wearedevelopers.com/videos/1597-self-hosted-llms-from-zero-to-inference) ## 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 And AI Driven Development](https://www.wearedevelopers.com/magazine/82-mlops-and-ai-driven-development) - [MLOps – What’s the deal behind it?](https://www.wearedevelopers.com/magazine/125-mlops-what-s-the-deal-behind-it) - [Coffee with Developers - Maria Apazoglou - Making AI understandable for all in production](https://www.wearedevelopers.com/magazine/475-coffee-with-developers-maria-apazoglou-making-ai-understandable-for-all-in-production) - [The Best Large Language Models on The Market](https://www.wearedevelopers.com/magazine/319-the-best-large-language-models-on-the-market)