Machine Learning Engineer Jobs in Melbourne, FL (NOW HIRING)

RELOCATION ASSISTANCE: No relocation assistance available

CLEARANCE REQUIRED FOR START: Yes

CLEARANCE TYPE: Secret

TRAVEL: Yes, 10% of the Time

At Northrop Grumman, our employees have incredible opportunities to work on revolutionary systems that impact people's lives around the world today, and for generations to come. Our pioneering and inventive spirit has enabled us to be at the forefront of many technological advancements in our nation's history - from the first flight across the Atlantic Ocean, to stealth bombers, to landing on the moon. We look for people who have bold new ideas, courage and a pioneering spirit to join forces to invent the future, and have fun along the way. Our culture thrives on intellectual curiosity, cognitive diversity and bringing your whole self to work — and we have an insatiable drive to do what others think is impossible. Our employees are not only part of history, they're making history.

At the heart of Defining Possible is our commitment to missions. In rapidly changing global security environments, Northrop Grumman brings informed insights and secure technological solutions to enable strategic objectives. We’re looking for innovators who can help us keep building on our wide portfolio of secure, affordable, integrated, and multi-domain systems and technologies that fuel those missions. By joining in our shared mission, we will support your mission of expanding your personal network and developing skills, whether you are new to the field or an industry thought leader. At Northrop Grumman, you will have the resources, support, and team to do some of the best work of your career.

Northrop Grumman's Corporate Chief Information & Digital Office (CIDO) is seeking a Principal Systems Engineer / Senior Principal Systems Engineer to support the Office of the Chief Engineer's IT Solutions team. The selected candidate will work as an IT Systems Engineer serving as a senior technical lead within the Systems Engineering & Architecture IPT and will be required to work on-site, full-time at our Melbourne, FL campus - this is NOT a remote work opportunity.

This individual will be responsible for architecting and delivering the Digital Engineering Ecosystem (DEE) — the integrated IT backbone that connects authoritative sources of truth, digital models, program data, and engineering tools across program lifecycle.

The DEE lead operates at the intersection of enterprise IT, systems engineering, digital transformation, AI/ML integration, and government compliance. This engineer works across every phase of the product development lifecycle — concept, design, integration, verification, and sustainment — while serving as the primary technical interface to the program's government customer and to Systems Engineering IPT leadership.

This position offers the opportunity to architect, lead, and deliver secure, modern IT infrastructure for a government-grade customer — driving digital engineering strategy, AI-enabled automation, and data-driven decision-making from the ground up.

This is not an entry-level role, a principal systems engineer is expected to independently lead technical workstreams, mentor junior engineers, interface directly with government customers and program leadership, and make architectural decisions that shape the DEE for the life of the program. Candidates with a foundation in artificial intelligence, machine learning integration, and intelligent automation will be especially competitive. If you are technically sharp, mission-focused, and ready to own outcomes — we want to hear from you.

• Lead the design, development, and governance of the Looking Glass Digital Engineering Ecosystem (DEE) architecture end-to-end.

• Define and enforce the DEE framework that connects Program Authoritative Sources of Truth (ASoTs) — data, models, documents — across all stakeholder communities.

• Identify and integrate AI-enabled capabilities into the DEE where appropriate — including intelligent data pipelines, automated anomaly detection, and predictive analytics on program data.

• Ensure DEE architecture enables digital engineering and sustainment access throughout the full program lifecycle.

• Own architectural decisions and produce authoritative architecture artifacts: ConOps, SRDs, SoS architecture diagrams, interface documents, and SoS-level agreements.

• Lead the IT technical baseline; develop and deliver the Systems Engineering Management Plan (SEMP).

• Apply Model-Based Systems Engineering (MBSE) using Cameo, Orbus, or equivalent — producing models that are consumable by both engineering and government stakeholders.

• Drive requirements definition, allocation, traceability, and management throughout the program lifecycle using DOORS and/or RTM.

• Lead and prepare program review artifacts for SRR, PDR, CDR, SVR, and TRR; present to IPT and government customer.

• Conduct trade studies, functional analysis, and market research to inform design decisions.

• Architect and implement scalable, redundant, highly available IT solutions across on-premises, co-location, and cloud environments.

• Lead VDI solution architecture and deployment (Nutanix, VMware Horizon) in support of DEE endpoints.

• Design and implement secure operating systems, networks, and COTS software solutions aligned to DEE requirements.

• Optimize cloud resource utilization for cost and performance; lead cloud migration planning as applicable.

• Evaluate and recommend AI/ML tools, platforms, and frameworks appropriate for integration into the DEE — including predictive analytics, anomaly detection, and process optimization use cases.

• Support the design of data pipelines and infrastructure (data lakes, model serving layers) that enable AI/ML workloads within compliant government IT boundaries.

• Apply AI-assisted automation to systems engineering workflows — including requirements analysis, test coverage assessment, and configuration management anomaly detection.

• Assess and document AI/ML system risks, including data governance, model explainability, and compliance with applicable DoD AI ethics guidelines (DoD AI Adoption Strategy, CDAO frameworks).

• Collaborate with data scientists, software engineers, and program stakeholders to translate AI capability needs into actionable DEE infrastructure requirements.

• Ensure DEE architecture and all technology solutions comply with Enterprise policies, Government requirements, and cyber compliance mandates (DFARS, NIST 800-53, CMMC 2.0).

• Lead or oversee the Infrastructure Configuration Control Board (CCB) process; ensure approved change management processes are followed.

• Own configuration management for all DEE hardware, software, and firmware; drive patch and upgrade compliance.

• Develop and maintain the Information Systems Security Plan (ISSP) for production and DEE systems.

• Lead periodic risk assessments; produce documented mitigations and present findings to program leadership.

• Serve as the primary IT/DEE technical interface to the government customer and SE&A IPT Lead.

• Drive collaboration across IT, cyber security, business management, and external vendors to ensure program milestones are met.

• Interface regularly with project managers, architects, technical specialists, and site directors.

• Mentor T01/T02 engineers; serve as technical consultant to IPT leads and program managers.

• Communicate complex technical content — written and verbal — to both technical and non-technical stakeholders.

• Work with IT Asset Management (ITAM) to maintain authoritative records for all DEE-managed hardware and software.

• Author and maintain SOPs, KBs, SRDs, interface documents, V&V plans, ConOps, and DR procedures.

• Track and enforce backup procedures, disaster recovery plans, and patch compliance across the DEE.

This requisition may be filled at either a Principal Systems Engineer (T03) or Senior Principal Systems Engineer (T04).

• 5 Years of relevant experience with a Bachelors degree in Engineering, Computer Science, or related STEM discipline; 3 Years with a Masters degree; 1 Year with a PhD.

• Candidates must have a current U.S. Government Secret level security clearance (or higher) in order to be considered.

• 1-5 years in IT Systems Engineering, Architecture, or Defense IT.

• Demonstrated experience designing or contributing to a Digital Engineering Ecosystem or similar program-level IT architecture.

• Proficient in MBSE tools (Cameo, Orbus, or equivalent); experience producing program-grade models.

• Hands-on experience with DOORS and/or requirements traceability matrices (RTM).

• Working knowledge of NIST 800-53, CMMC 2.0, DFARS, and applicable DoD IT standards.

• Demonstrated ability to lead working groups, technical interchange meetings, and present at formal program reviews.

• Familiarity with AI/ML concepts, tools, or platforms; experience applying AI-enabled automation or analytics in an engineering or IT context is a plus.

• 8 Years of relevant experience with a Bachelors degree in Engineering, Computer Science, or related STEM discipline; 5 Years with a Masters degree; 3 Year with a PhD.

• Candidates must have a current U.S. Government Secret level security clearance (or higher) in order to be considered.

• 5-8 years in IT Systems Engineering, Architecture, or Defense IT.

• Demonstrated experience designing or contributing to a Digital Engineering Ecosystem or similar program-level IT architecture.

• Expertise in MBSE tools (Cameo, Orbus, or equivalent); experience producing program-grade models.

• Hands-on experience with DOORS and/or requirements traceability matrices (RTM).

• In-depth knowledge of NIST 800-53, CMMC 2.0, DFARS, and applicable DoD IT standards.

• Demonstrated ability to lead working groups, technical interchange meetings, and present at formal program reviews.

• Expertise with AI/ML concepts, tools, or platforms; experience applying AI-enabled automation or analytics in an engineering or IT context is a plus.

• Candidates must have the ability to obtain, and maintain, a U.S. Government Top Secret level security clearance as a condition of continued employment.

• Candidates must have the ability to obtain, and maintain, SCI level access as a condition of continued employment.

• Experience designing data/model governance frameworks that ensure stakeholder access to authoritative program data across the lifecycle.

• Familiarity linking MBSE tools (Cameo, Orbus) to DEE infrastructure — PLM, PDM, document management, and collaboration platforms.

• Understanding of digital thread architecture and how DEE enables program-wide traceability from requirements through sustainment.

• Proficiency with VMware Horizon, Nutanix, or equivalent — including deployment and lifecycle management for DEE endpoints.

• Hands-on experience with AWS, Azure, or GCP; cloud cost optimization; hybrid cloud architecture in classified/unclassified environments.

• Advanced knowledge of TCP/IP, VLANs, routing, firewall architecture, and network segmentation for enterprise environments.

• Experience with on-premises and co-location design; HA, redundancy, backup, and disaster recovery planning.

• MBSE Tools — Cameo Systems Modeler, Orbus iServer, or equivalent (T04: advanced user/model owner preferred).

• Requirements Management — DOORS, DOORS Next Generation, or RTM-based approaches; traceability across lifecycle milestones.

• Program Reviews — Experience leading or co-leading SRR, PDR, CDR, SVR, TRR artifact preparation and presentations.

• Risk Management — Formal risk identification, documentation, and mitigation tracking in program risk registers.

• Working knowledge of supervised/unsupervised learning concepts, model training pipelines, and common frameworks (TensorFlow, PyTorch, scikit-learn, or equivalent).

• Familiarity with AI/ML deployment patterns — model serving, inference infrastructure, and MLOps practices within enterprise IT environments.

• Experience or interest in applying AI-assisted tools to SE workflows: requirements analysis automation, NLP-based traceability, intelligent test coverage, or MBSE model generation support.

• Understanding of how large language models (LLMs) and generative AI tools can responsibly augment engineering productivity within a classified or government-adjacent environment.

• Familiarity with predictive models, anomaly detection on data streams, or AI-driven decision-support tools integrated into program operations.

• Experience surfacing AI/ML outputs through dashboards or operational tools used by engineering or program personnel.

• Understanding of data lake / data warehouse architecture, ETL/ELT pipelines, and the infrastructure requirements for training and serving AI/ML models.

• Awareness of data governance, lineage, and quality requirements that underpin trustworthy AI — particularly within DoD and ITAR-sensitive environments.