Middle/Senior Machine Learning Engineer

We are looking for a Middle/Senior Machine Learning Engineer to develop and enhance AI-driven solutions within the Palantir Foundry and AIP ecosystem. In this role, you will focus on building and iterating on machine learning and LLM-based solutions, integrating them into Foundry workflows to support analytics, automation, and decision-making. You will collaborate closely with data engineers, business analysts, and domain experts to deliver practical, production-ready AI solutions.

Key Responsibilities: Develop and enhance machine learning and AI models to support predictive analytics, classification, forecasting, and AI-assisted workflows. Build AI and ML solutions within Palantir Foundry, using Python and existing Foundry pipelines, Ontology objects, and workflows. Apply LLMs and NLP techniques (e.g. prompt engineering, fine-tuning, embeddings, retrieval-augmented workflows) using Palantir AIP for enterprise use cases. Collaborate with data engineers to understand data sources, ensure data quality, and prepare datasets for model training and inference. Conduct experiments, evaluate model performance, and iterate on features and model approaches. Integrate AI models into Foundry workflows to surface insights and support business processes. Support model deployment and monitoring by following established team standards and best practices. Work closely with business and domain stakeholders to translate requirements into practical AI-driven solutions. Document model behavior, assumptions, and limitations to support transparency and compliance. Stay up to date with applied AI and GenAI trends and contribute ideas under guidance from senior team members. Requirements:

3+ years of experience in machine learning, AI engineering, or applied data science. Strong Python skills; experience with ML libraries such as scikit-learn, XGBoost, TensorFlow, or PyTorch. Hands-on experience with LLMs, NLP, or GenAI use cases (e.g. prompt design, embeddings, text classification, summarization). Practical understanding of the ML lifecycle: data preparation, feature engineering, model training, evaluation, and iteration. Experience working with structured data (tabular, time series); exposure to text or unstructured data is a plus. Familiarity with enterprise data environments and collaborative development workflows. Ability to clearly explain model results and AI behavior to non-technical stakeholders. Upper-Intermediate English or higher.

Nice to have: Proficiency in Foundry Ontology, Object Builders, and Code Repositories. Experience in big pharma or highly regulated industries. Knowledge of data, compliance, and security best practices in AI applications. Familiarity with cloud platforms (AWS, GCP, or Azure) and containerization (Docker, Kubernetes).