Staff Software Engineer

Merck•Saint Louis, MO

7d•Hybrid

About The Position

We are looking for a Staff Applied AI Engineer to build scalable, production-grade backend applications that leverage large and complex datasets. This role is software engineering first, with a strong emphasis on designing reliable systems that ingest, process, and serve data to power modern applications and AI-driven solutions. You will work closely with product managers, software engineers, data scientists, and ML engineers to build robust backend services, data-intensive applications, and production AI systems. Success in this role requires strong software engineering fundamentals, practical experience working with data throughout its lifecycle, and the ability to design systems that are scalable, maintainable, and reliable in production.

Requirements

Bachelor’s degree in Computer Science, Engineering, Data Science, or a related quantitative field.
At least 3 years of hands-on experience in machine learning, data science, search relevance, or ranking systems.
10+ years of software engineering experience, with deep recent time leading production AI/LLM systems
Proven expertise in Python and ML frameworks (MLFlow, TensorFlow, PyTorch, Scikit- learn, or equivalent).
Strong background in statistical analysis, data exploration, and working with large-scale datasets.
Experience with feature engineering, data preprocessing, and data
Seasoned hands-on coder; still writes production Python regularly
Seasoned system designer for AI systems at scale — retrieval, agents, evaluation, latency, and cost, vector databases/pipelines
Strong experience building and maintaining production-grade backend applications.
Experience designing and developing RESTful APIs and distributed systems.
Strong SQL skills and experience working with relational databases; familiarity with NoSQL databases or modern data storage technologies is a plus.
Solid understanding of data engineering fundamentals, including data quality, validation, transformation, modeling, and efficient storage.
Experience designing systems that process large datasets reliably and efficiently.
Experience with cloud platforms such as Google Cloud Platform (GCP) or AWS.
Experience using Docker, Git, CI/CD pipelines, automated testing frameworks, and modern software engineering best practices.
Core engineering stack Languages: Python, REST API, Pandas, NumPy
Cloud and infrastructure: AWS Services and/or GCP, Kubernetes, Bedrock
Distributed systems: event-driven architectures, including Kafka
Orchestration Frameworks: LangGraph, LangChain, AirFlow, etc.
Vector Databases like Qdrant

Nice To Haves

Experience with Kubernetes and container orchestration.
Familiarity with event-driven architectures and messaging platforms such as Kafka.
Familiarity with ML model deployment and inference pipelines

Responsibilities

Lead technical strategy for AI/LLM systems across multiple products
Architect retrieval, orchestration, agentic, and evaluation systems that run reliably in production
Set the standards for AI safety, evaluation, observability, and responsible rollout in a regulated context
Mentor Junior-level engineers into strong AI engineers; Employ AI Native development skills to multiply the productivity (Claude, etc.)
Lead the frontier: evaluate new models, techniques, and tools, and bring the right ones into the team
Design, develop, and maintain scalable Python applications and backend services.
Build systems that ingest, validate, transform, and manage structured and unstructured data in production environments.
Design data models and storage solutions that support scalable, high-performance applications.
Develop reusable components for data processing, validation, enrichment, and feature generation.