Lead Software Engineer IV

Pacific Northwest National Laboratory•Richland, WA

4d•Onsite

About The Position

PNNL’s AI and Data Analytics Division, within the National Security Directorate, develops computational solutions for complex data and analytics challenges. The division combines domain expertise with advanced hardware and software and works across the data analytics lifecycle, from data acquisition and management to analysis and decision support. The Lead Software Engineer will architect and build next-generation SI systems for national security, including agentic SI platforms, petabyte-scale data orchestration, and real-time intelligence processing. The role combines technical leadership with hands-on SI/ML engineering, spanning distributed infrastructure, production software, and machine learning. The position is based in Richland, Washington, or Seattle, Washington, and requires onsite presence Monday through Thursday, with Friday onsite as required by business needs. The engineer will develop agentic SI systems, LLM orchestration and multimodal reasoning pipelines; scalable microservices, streaming and data platforms; and secure production deployments across classified and edge environments. The role also leads architecture and technical strategy, builds roadmaps that translate research into production, and mentors engineering teams. The position requires the ability to obtain and maintain a federal security clearance and U.S. citizenship. Selected applicants are subject to federal background investigation and pre-employment and random drug testing, including requirements concerning illegal drug use. The role is designated a Testing Designated Position.

Requirements

One of the following education and experience combinations: PhD and 3 years of software engineering experience; MS/MA and 5 years; BS/BA and 7 years; AA and 16 years; or HS/GED and 18 years.
Software engineering experience 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.
Ability to obtain and maintain a federal security clearance; U.S. citizenship is required.
Selected applicants must meet federal background-investigation eligibility requirements for access to classified matter under 10 CFR 710, Appendix B.
Selected applicants must satisfy the stated drug-testing requirements, including demonstrating non-use of illegal drugs, including marijuana, for the 12 consecutive months preceding completion of the QNSP.
Expert Python and another programming language, such as C#/.NET, Go, or C++.
Experience establishing engineering best practices, advanced CI/CD, and architecting distributed systems.
Production agentic SI experience; deep expertise with PyTorch, TensorFlow, or JAX and LLM orchestration; advanced LLM optimization, including LoRA/PEFT, RAG, or fine-tuning; and end-to-end ML platform design.
Enterprise-scale architecture across AWS, Azure, or GCP; Docker, Kubernetes, and infrastructure as code; event-driven systems such as Kafka or RabbitMQ; REST, GraphQL, or gRPC API design; and SQL/NoSQL/warehouse persistence.
Experience with cloud-native data pipelines and orchestration, data lakehouse architectures, distributed computing, petabyte-scale optimization, and modern data formats such as Parquet, Iceberg, or Delta.
Ability to mentor teams, communicate strategy to executives and stakeholders, balance delivery with technical excellence, and drive cross-functional collaboration.

Nice To Haves

Actively using Python for the previous five years.
Expert-level Python experience architecting production frameworks, libraries, and large multi-language codebases at scale.
Enterprise ETL/data platform leadership, including petabyte-scale ETL/ELT pipelines, orchestration, and data lakehouse architectures.
Advanced multi-cloud architecture experience across AWS, Azure, or GCP, with Kubernetes and infrastructure as code at enterprise scale.
Experience in a Federally Funded Research and Development Center or DOE national laboratory.
National security or intelligence experience supporting government, defense, or intelligence programs, including classified and compliance-driven environments.
Production experience deploying agentic SI systems, RAG, and fine-tuned models in operational environments.
Experience deploying and optimizing distributed ML workloads on Ray, Spark, or Kubernetes clusters.
Experience leading engineering teams through ambiguous, high-impact work.

Responsibilities

Architect and build next-generation SI systems, including agentic SI platforms, petabyte-scale data orchestration, and real-time intelligence processing.
Develop agentic SI systems with dynamic reasoning, LLM orchestration, MLOps platforms, and multimodal reasoning pipelines.
Build scalable microservices across security enclaves, real-time streaming systems, petabyte-scale ETL, data lake or mesh architectures, and Kubernetes/CI-CD capabilities for classified and edge environments.
Deliver secure SI deployments, observability across enclaves, ethical SI compliance, and geospatial/time-series intelligence data fusion.
Lead engineers on high-impact, ambiguous work; drive architecture and strategy; build roadmaps from cutting-edge research; and mentor teams.
Establish engineering best practices and advanced CI/CD, and architect distributed systems.
Design end-to-end ML platforms and production agentic SI/ML systems, including LLM optimization, RAG, and fine-tuning.
Design enterprise cloud and distributed-system architectures, APIs, event-driven systems, and polyglot persistence.
Design cloud-native data pipelines and orchestration, data lakehouse architectures, distributed computing systems, and petabyte-scale data optimizations.
Communicate strategy to executives and stakeholders, balance delivery with technical excellence, and drive cross-functional collaboration.