Associate, AI/ML Engineer

Confirmed live in the last 24 hours

Legend Biotech

Somerset, New Jersey, United States

Hybrid

Posted March 27, 2026

Job Description

Legend Biotech is a global biotechnology company dedicated to treating, and one day curing, life-threatening diseases. Headquartered in Somerset, New Jersey, we are developing advanced cell therapies across a diverse array of technology platforms, including autologous and allogenic chimeric antigen receptor T-cell, T-cell receptor (TCR-T), and natural killer (NK) cell-based immunotherapy. From our three R&D sites around the world, we apply these innovative technologies to pursue the discovery of safe, efficacious and cutting-edge therapeutics for patients worldwide.

Legend Biotech entered into a global collaboration agreement with Janssen, one of the pharmaceutical companies of Johnson & Johnson, to jointly develop and commercialize ciltacabtagene autolecuel (cilta-cel). Our strategic partnership is designed to combine the strengths and expertise of both companies to advance the promise of an immunotherapy in the treatment of multiple myeloma.

Legend Biotech is seeking an Associate, AI/ML Engineer as part of the IT team based in Somerset, NJ.

Role Overview

We are seeking a motivated and curious Associate AI/ML Engineer to join our Data & AI team. In this role, you will help design, build, test, and deploy production-grade Generative AI solutions—leveraging large language models (LLMs), retrieval-augmented generation (RAG), and agentic workflows—along with traditional machine learning models that support business and product goals. This is an excellent opportunity for early-career professionals to gain hands-on experience in building safe, reliable AI applications in real-world environments.

Key Responsibilities

Build and iterate on LLM-powered features (e.g., chat, summarization, extraction, classification) using approved model providers and internal platforms.
Design retrieval-augmented generation (RAG) solutions, including document ingestion, chunking, embeddings, vector search, and prompt templates.
Develop and evaluate prompts, tools, and agentic workflows; implement guardrails (grounding, citations, refusal behavior) and improve reliability.
Create offline and online evaluation for GenAI systems (test sets, automated metrics, human review processes) and track quality over time.
Assist with developing, training, and evaluating traditional ML models when needed (e.g., classification, regression, anomaly detection, recommendation).
Clean, preprocess, and analyze structured and unstructured data; build reproducible data pipelines for model inputs and evaluation.
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