Senior Software Engineer III

Pacific Northwest National Laboratory•Richland, WA

69d•Hybrid

About The Position

At PNNL, our core capabilities are divided among major departments that we refer to as Directorates within the Lab, focused on a specific area of scientific research or other function, with its own leadership team and dedicated budget. Our Science & Technology directorates include National Security, Integrated Discovery Sciences, and Energy and Environment. In addition, we have an Environmental Molecular Sciences Laboratory, a Department of Energy, Office of Science user facility housed on the PNNL campus. The National Security Directorate (NSD) drives science-based, mission-focused solutions to take on complex, real-world threats to our nation and the world. The AI and Data Analytics Division, part of the National Security Directorate, combines profound domain expertise and creative integration of advanced hardware and software to deliver computational solutions that address complex data and analytic challenges. Working in multidisciplinary teams, we connect foundational research to engineering to operations, providing the tools to innovate quickly and field results faster. Our strengths are integrated across the data analytics lifecycle, from data acquisition and management to analysis and decision support.

Requirements

Strong Python plus one other language (.NET/C#, Go, C++, Rust).
Solid CS fundamentals (object-oriented design, design patterns, data structures, algorithms, and clean code practices).
Git.
Testing (TDD).
CI/CD.
AI-assisted development tools (Copilot, Claude, Cursor).
Understanding of the ML lifecycle, model serving, and production integration via APIs.
Hands-on experience with AWS/Azure/GCP, Docker/Kubernetes, event-driven & microservice architectures, API design (REST, GraphQL, gRPC), and SQL/NoSQL databases.
ETL/ELT pipelines, distributed processing (Spark, Kafka, Flink, Ray), cloud storage, and data modeling at scale.
Strong communication and engagement with cross-functional teams.
Agile experience.
Mentorship.
Pragmatic delivery.
PhD and 1 year of Software Engineering experience -OR- MS/MA and 3 years of Software Engineering experience -OR- BS/BA and 5 years of Software Engineering experience -OR- AA and 14 years of Software Engineering experience in designing, architecting, programming, deploying, and automating software solutions in support of scientific research or consumer digital product development -OR- HS/GED and 16 years of Software Engineering experience in designing, architecting, programming, deploying, and automating software solutions in support of scientific research or consumer digital product development.
Current, hands-on software development experience using Python.
U.S. Citizenship.
Ability to obtain and maintain a federal security clearance.
Ability to pass a federal background investigation.
Ability to pass pre-employment drug testing and post-employment random drug testing.
Ability to demonstrate non-use of illegal drugs, including marijuana, for the 12 consecutive months preceding completion of the requisite Questionnaire for National Security Positions (QNSP).

Nice To Haves

Actively using Python and have been doing so for the previous three years.
Advanced Python expertise: Building production-grade applications, libraries, or frameworks at scale.
ETL/data pipeline experience: Designing and optimizing large-scale ETL/ELT workflows using cloud-native tools (AWS Glue, Azure Data Factory, Step Functions) and distributed frameworks (Spark, Kafka).
Deep cloud platform experience: Production deployments on AWS, Azure, or GCP, with multi-cloud and Kubernetes orchestration highly valued.
FFRDC or national laboratory experience: Prior work within a Federally Funded Research and Development Center or DOE national lab environment.
National security / mission-driven experience: Supporting government, defense, or national security programs, including familiarity with secure and compliance-driven environments.
Agentic AI / LLM experience: Hands-on work with LLM orchestration, RAG, or autonomous agent frameworks.
MLOps maturity: Experience operationalizing ML models with experiment tracking, versioning, and monitoring in production.

Responsibilities

Design scalable, mission-critical solutions while mentoring others and shaping technical direction.
Lead complex system components.
Mentor junior engineers.
Turn technical requirements into working solutions.
Build AI Systems: Agentic AI, LLM orchestration (LangChain, LlamaIndex), MLOps pipelines, and multi-modal data integration.
Build Scalable Infrastructure: Microservices, real-time streaming, large scale ETL pipelines, and Kubernetes deployments.
Build Production Systems: Monitoring/observability, secure & edge deployments, geospatial and time-series processing.
Provide Technical Leadership: Lead project components, mentor staff, drive design decisions, and support proposals.