Data Scientist - Machine Learning Engineer, LLM & RAG Systems

February 7

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Lynceus

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Lynceus is a company focused on optimizing manufacturing productivity using AI technology. They provide real-time visibility into the outcomes of critical manufacturing processes, allowing engineers to detect failures early and troubleshoot quickly. Lynceus offers products like Augmented Metrology and Process Behavior Monitoring, helping manufacturers improve quality, throughput, and costs simultaneously. Their solutions emphasize minimal additional capital expenditures and workforce requirements, catering to efficient and advanced manufacturing environments.

AI • Machine Learning • semiconductor manufacturing

11 - 50 employees

🤖 Artificial Intelligence

💰 Seed Round on 2020-12

📋 Description

• Develop and optimize RAG pipelines to enhance LLM-driven troubleshooting. • Work with PyVector to store and retrieve relevant data efficiently. • Implement vector embedding models to power retrieval and improve accuracy. • Build high-quality Python code to power our AI applications. • Deploy and scale models on AWS (S3, Lambda, ECS, SageMaker, Bedrock). • Fine-tune and evaluate LLMs for task-specific reasoning and accuracy. • Implement efficient document indexing and retrieval for knowledge-intensive queries. • Experiment, iterate, and deploy features quickly based on real customer feedback. • Work with a small but highly skilled team to push the limits of industrial AI. • Take ownership of challenges, figure things out as you go, and adapt to changing priorities.

🎯 Requirements

• Strong Python development skills, with experience in machine learning or NLP. • Experience with RAG architectures and knowledge retrieval techniques. • Familiarity with PyVector for vector search and retrieval. • Experience using vector embedding models (e.g., OpenAI, Bedrock Titan, Cohere, SentenceTransformers). • Understanding of embedding models and best practices for retrieval. • Experience working with AWS for cloud-based ML deployment. • Knowledge of LangChain, LlamaIndex, or other RAG frameworks. • Ability to work in a fast-paced, experimental environment, where iteration and adaptation are key. • A self-starter who can work autonomously in a remote setting. • A strong problem-solver who thrives when thrown in at the deep end.