AI/ML Engineer (Clinical Data Intelligence Platform)
Company Overview
We are building a generative AI–powered Clinical Data Intelligence Platform that helps pharmaceutical companies and research institutions complete FDA submissions more efficiently, compliantly, and intelligently. The platform automates the processing of real-world and clinical trial data.
Our mission: make standardization, analysis, and submission more efficient and intelligent, accelerating drug development and innovation in life sciences.
Position Title
AI/ML Engineer
Focus on LLM + Multi-Agent Systems + Compliance Data Engineering, targeting senior / staff / principal-level engineers capable of driving deployment and scalability in complex, high-uncertainty environments.
Job Description
• Design and implement LLM / generative modeling algorithms and systems for clinical data processing, validation, and review.
• Research and deploy multi-agent architectures (reasoning, planning, tool use, self-verification) to solve complex data transformation, auditing, and traceability problems.
• Build and optimize end-to-end data pipelines.
• Collaborate across Biostatistics, Data Management, and Platform Engineering teams to align with FDA/CDISC compliance standards.
• Develop evaluation and security mechanisms (data anonymization, access control/auditing, model safety boundaries, benchmark evaluation) to continuously enhance system reliability, interpretability, and compliance.
• Perform system-level trade-offs among performance, cost, and maintainability, and drive engineering scalability through service-oriented, containerized, and horizontally scalable solutions.
Qualifications
Required
• Solid background in Machine Learning, proficient in PyTorch / TensorFlow or similar deep learning frameworks.
• Strong Python engineering skills, capable of writing readable, testable, and maintainable production-grade code.
• Experience or research in Agentic AI / Multi-Agent systems, with understanding of reasoning, multi-step task planning, and self-feedback mechanisms.
• Strong ability to read and comprehend technical and regulatory English documentation, including FDA/CDISC standards.
• Familiar with LLM application engineering, including prompt engineering, function/tool calling, retrieval-augmented generation (RAG), evaluation and alignment, model training (fine-tuning, RL, etc.), and inference optimization (quantization, batch processing, hardware acceleration).
Preferred
• Understanding of clinical trial data workflows and the SAS ecosystem, with collaboration experience in statistical programming / biostatistics.
• Familiar with pharmaceutical compliance, including FDA submission process, 21 CFR Part 11, GxP, and data traceability & audit.
• Proven experience deploying and operating AI/ML systems in high-compliance industries (e.g., healthcare, finance, government).
Competency Model
Senior Engineer
• Independently responsible for complex subsystems/features; capable of driving delivery under incomplete requirements.
• Technically lead small teams, balancing performance, stability, and compliance in solution design.
• Mentor junior and mid-level engineers, establishing engineering best practices.
Staff Engineer
• End-to-end ownership of key product domains; define technical roadmap and evolution plan.
• Align across teams (Biostat/DM/Platform/Compliance), resolving ambiguity and dependencies.
• Drive organizational quality and efficiency through metrics and evaluation frameworks, generating broad organizational impact.
Senior Staff / Principal Engineer
• Set domain-level technical vision and milestones; lead architecture modernization, platformization, and scaling.
• Innovate within security and compliance boundaries, establishing industry-standard best practices.
• Build and mentor technical talent pipelines, shape company-wide engineering culture and standards, and create external impact (papers, talks, standard contributions).
Key Tech Stack (Examples)
• LLM & Agent: Integration of OpenAI and Claude APIs, along with open-source foundation models (Qwen, Llama, Mistral) and leading agentic frameworks (LangChain, LlamaIndex, self-developed tool-use and control loops)
• Frameworks: PyTorch/TensorFlow, vector databases
• LLM Optimization: LoRA / QLoRA, continuous batching, distributed inferencing
What We Offer
• Highly competitive compensation (cash + equity), benchmarked against global top talent.
• Rapid growth opportunities at the frontier of AI × Clinical Research, tackling real-world scale and compliance challenges.
• Results-driven engineering culture: efficient, pragmatic, and open, emphasizing verifiable business and compliance value.
• Real-world impact: your work will directly improve the efficiency and quality of FDA submissions.
Join Us
If you’re passionate about creating real impact at the intersection of Life Sciences × Generative AI, and want to define and build the next-generation Clinical Data Intelligence Platform, please contact:
hr@hillresearch.ai
