Remote Data Jobs • LLM

Research, prototype, and productionize generative AI models. Develop scalable GenAI pipelines that generate high-quality content, from product descriptions, reviews, titles, and other product content. Design and evaluate prompt tuning strategies and RAG systems to ensure factual and engaging outputs. Fine-tune foundation models and develop domain-specific adapters using techniques like LoRA, PEFT, and instruction tuning.

This role sits at the intersection of quantitative finance, machine learning, and natural-language understanding. You’ll leverage NLU techniques—like sentiment and intent analysis—to mine news and social media for trading signals, build and mathematically refine predictive models, and rigorously backtest and optimize your strategies to drive data-driven investment decisions.

During this Master thesis, you will develop and train an ML model to parse industry-specific documents as an entry point for a Retrieval-Augmented Generation (RAG) pipeline. Your job includes the development of custom parsing and chunking tools for industry-specific documents, as well as their ingestion into a vector database. Last but not least, you will train and/or fine-tune a custom LLM model for use-case-specific applications within the scope of smart hydraulic services.

Magnify needs problem solvers who like to automate and optimize, enjoy simplifying complexity and know how to experiment. We’re looking for a ML / Applied Science Tech lead who is a pragmatic leader and who can translate business needs into workable science solutions. As one of the first hires at a venture-track company, this role provides an opportunity to grow with the business, and to have real impact with enormous upside.

Develop robust, scalable ML software for predictive and generative modeling tasks related to genomics data (eg. Interactome, Cell & Tissue modeling). Design and implement ML algorithms to enhance NGS sequencing pipelines. Apply reasoning techniques—including LLMs, Graph Neural Networks, Gen AI models—for extracting insights to advance drug discovery from simulation, omics data, and literature.

The Custom Models team is responsible for Duo Self-Hosted, a key component in GitLab AI that allows customers to run GitLab Duo features on completely private environments, connecting GitLab to their own AI models. Develop evaluation techniques to assist feature teams on guaranteeing the quality of their features on new models. Evolve the Evaluation Runner, our internal tool for scaling AI Feature evaluation.