Lead Software Engineer IV

Pacific Northwest National Laboratory•Richland, WA

5d•$161,300 - $255,000•Onsite

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, Earth and Biological Sciences, Physical and Computational 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 NSD, 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

Demonstrated fluency in Python and proficiency in at least one additional language (C#/.NET, Go, C++) with ability to architect solutions and guide language selection decisions across complex, multi-language codebases
Proven track record of establishing and championing software engineering best practices including version control strategies, comprehensive automated testing frameworks, code quality standards, and technical documentation across engineering teams
Expert-level proficiency in designing and implementing sophisticated CI/CD pipelines with ability to define DevOps strategies, build/release processes, and deployment architectures that ensure reliable, secure, and efficient software delivery at scale
Seasoned practitioner with ability to lead engineering teams in defining technical specifications, architectural patterns, and system designs for microservices, distributed systems, and large-scale applications while strategically leveraging AI assist tools to accelerate team productivity and drive innovation
Proven experience architecting, implementing, and deploying production-grade agentic AI systems with multi-step reasoning, autonomous workflows, and decision-making capabilities into operational environments at scale
Deep practical expertise with deep learning frameworks (PyTorch, TensorFlow, JAX) and LLM orchestration platforms (LangChain, LlamaIndex, LangGraph) with ability to design complex AI applications, custom chains, retrieval systems, and agent-based architectures
Advanced expertise in LLM optimization techniques including fine-tuning methodologies (LoRA/PEFT, QLoRA), retrieval-augmented generation (RAG) system design, prompt engineering strategies, and comprehensive evaluation frameworks
Comprehensive understanding of the end-to-end machine learning lifecycle with proven ability to architect and build production ML platforms including feature engineering pipelines, model serving infrastructure, monitoring, and automated retraining systems
Demonstrated expertise architecting and deploying enterprise-scale applications across cloud platforms (AWS, Azure, GCP) with ability to design multi-cloud strategies and advanced proficiency in containerization (Docker) and orchestration technologies (Kubernetes) including Infrastructure as Code practices
Expert ability to architect and implement sophisticated event-driven systems using message brokers (Kafka, RabbitMQ), pub/sub patterns, and serverless functions with consideration for exactly-once semantics, ordering guarantees, and failure handling
Mastery of cloud native API design patterns including RESTful principles, GraphQL schemas, and gRPC services with proven experience establishing API standards, versioning strategies, and microservice communication patterns for large-scale distributed systems
Deep understanding of data storage architecture including relational databases (PostgreSQL, MySQL), NoSQL systems (MongoDB, DynamoDB, Cassandra), and data warehouses (Redshift, Snowflake, BigQuery) with ability to design polyglot persistence strategies optimized for specific workload characteristics
Mastery of cloud-native data pipeline architectures including ETL/ELT design patterns, orchestration frameworks (Airflow, Prefect, Step Functions), and cloud services (AWS Glue, Lambda, Azure Data Factory) with ability to architect enterprise-scale data platforms
Expert knowledge of distributed data storage systems (S3, Redshift, Delta Lake, PostgreSQL, MongoDB, OpenSearch, Databricks) with proven ability to design data lakehouse architectures and advanced proficiency with distributed computing frameworks (Spark/Databricks, Kafka, Flink, Ray)
Demonstrated expertise deploying and optimizing scalable ML workloads on distributed platforms using Kubernetes, Ray clusters, or Spark with deep understanding of data modeling principles including schema design, normalization/denormalization strategies, and data quality frameworks
Proven ability to architect petabyte-scale data systems with appropriate partitioning strategies, indexing approaches, and query optimization patterns while mastering data format selection (Parquet, Avro, ORC, Delta, Iceberg) for optimal compression, performance, and schema evolution
Proven ability to lead and mentor engineering teams through technical challenges, architecture discussions, and knowledge sharing while establishing team standards for code quality, testing practices, and architectural patterns through mentorship and leading by example
Expert communication skills to articulate complex technical concepts, system designs, and strategic recommendations to diverse audiences including engineering teams, executive leadership, and stakeholders through comprehensive documentation, architecture decision records, and presentations
Strategic ability to balance competing priorities including technical excellence, delivery velocity, technical debt management, and innovation while making pragmatic trade-offs that align with organizational objectives
Experience leading technical planning initiatives including system architecture design, technology evaluation, and roadmap development with proven capability to drive cross-functional collaboration and champion process improvements while demonstrating adaptability to rapidly evolving technical landscapes
PhD and 3 years of Software Engineering experience -OR- MS/MA and 5 years of Software Engineering experience -OR- BS/BA and 7 years of Software Engineering experience -OR- AA 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 -OR- HS/GED and 18 years of Software Engineering experience in designing, architecting, programming, deploying, and automating software solutions in support of scientific research or consumer digital product development
U.S. Citizenship

Nice To Haves

Degree in computer science, software engineering, or related field
7+ years of professional software engineering experience with at least 3-5 years in technical leadership or senior engineering roles
Track record of leading development of production systems serving significant user bases or processing substantial data volumes
Experience building and leading high-performing engineering teams through mentorship and professional development
Demonstrated experience leading teams of software engineers and translating complex technical problems into structured, actionable work
Experience establishing engineering practices, architectural standards, and technical strategies at organizational scale
Background in multiple domains (AI/ML, distributed systems, data engineering, cloud infrastructure) with ability to bridge technical disciplines
Prior experience in mission-critical, regulated, or high-security environments (government, defense, healthcare, financial services)
Established thought leadership and technical influence through substantial open-source maintainer ship, published technical articles or conference talks, recognized expertise in specific domains, community building initiatives, or side projects that have driven innovation—demonstrating sustained commitment to advancing the profession and elevating others in the technical community

Responsibilities

Design and deploy scalable agentic AI systems with dynamic reasoning and decision-making capabilities
Architect LLM orchestration frameworks using LangChain, LlamaIndex, and emerging agent platforms
Build MLOps platforms spanning experiment tracking, model versioning, deployment, and governance
Develop developer-focused tooling, adapters, and interfaces for AI-native frameworks
Integrate multi-modal data sources (text, vision, structured/sensor data) into cohesive reasoning pipelines
Design microservices architectures coordinating across multiple domains and security enclaves
Lead distributed system design processing data from hundreds of sources simultaneously
Architect real-time streaming platforms handling terabytes per hour with event-driven architectures
Build robust data pipelines for petabyte-scale ETL, data lake/mesh architectures, and real-time analytics
Design container orchestration (Kubernetes) and CI/CD pipelines for classified and edge environments
Deploy AI systems in highly secure environments with resilient agent-to-agent communications
Create monitoring and observability systems (logging, metrics, tracing) across secure enclaves
Ensure compliance with ethical AI standards and security-first DevOps practices
Build geospatial processing, time-series, and intelligence data fusion capabilities
Lead a team of engineers to deliver on high risk / high impact ambiguous technical scope
Drive technical strategy and architectural decisions across cross-functional teams
Translate ambiguous requirements and cutting-edge research into actionable technical roadmaps
Lead design discussions shaping team-wide engineering standards
Mentor engineering teams and guide junior scientists/engineers