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Autonomous Systems Project Engineer- Launched Effects
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Remote Systems Integration Engineer - SME
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…infrastructures, working alongside leaders in aviation, engineering, data science, and systems…
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Mechanical Engineer Level 4/5
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Data Engineer Intern – Summer 2027 – Onsite in Dallas, TX
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Senior Security Architect
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Lead AI/ML Ops Engineer
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4 Year/Bachelor's degree in Computer Science, Computer Engineering, Information Systems, Data Science, or related discipline, or 4 years of equivalent work experience in lieu of Bachelor's.
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Sr AI/ML Ops Engineer
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Analyst, Data & Software Engineer
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Digital Communication DSP Engineer
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Senior Foundry Integration Engineer
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2027 Spring Process Engineer Co-op - Doctorate (Gloucester, MA)
Applied Materials
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Applied Materials is the global leader in materials science and engineering solutions that are at the foundation of virtually every new semiconductor chip and advanced display in the world.…

Synopsys

Principal Application Engineer- Missile Defense

USA Off-Site, CO, United States

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Job details

Experience: BS in Engineering, Physics, Computer Science, or related field with 10+ years of professional experience, or MS with 8+ years
Posting language: English
Posted: Nov 6, 2026

Original job description

***Currently, we are searching for experienced candidates in the Colorado Springs, CO area or Exton, PA area.***

We Are Synopsys is the leader in engineering solutions from silicon to systems, enabling customers to rapidly innovate AI-powered products. We deliver industry-leading silicon design, IP, simulation and analysis solutions, and design services. We partner closely with our customers across a wide range of industries to maximize their R&D capability and productivity, powering innovation today that ignites the ingenuity of tomorrow. You Are You have spent years building simulation environments that model what actually happens when a missile launches, not what should happen in theory. You understand that the difference between a useful threat model and an academic exercise is whether it accounts for the messy reality of radar cross-sections changing during staging events, IR signatures evolving through flight phases, and sensors trying to make sense of it all in real time. You are comfortable translating a customer's operational problem into a simulation architecture that can answer it. When someone asks whether a new sensor configuration can detect a specific threat class, you know which physics matter, which can be approximated, and how to build the model that gives them a real answer. You have worked with actual radar data, not just textbook equations, and you know what X-band sees differently than S-band because you have debugged the models that got it wrong. You think in kill chains. Detection, tracking, discrimination, engagement decisions, the whole sequence matters, and you build simulations that respect those dependencies. You also know that the synthetic data you generate today will train the ML models that make those decisions tomorrow, so you care deeply about fidelity, edge cases, and bias. At Synopsys, you will work with defense customers who need your expertise to make hard decisions about billion-dollar systems. Your models will matter. What You'll Be Doing

Build high-fidelity simulation environments for air and missile defense systems, modeling threat trajectories, staging events, and multi-modal signatures across RF, IR, and other sensor-observable spectra
Model radar phenomenology and sensor architectures, representing how different radar bands and configurations observe and interpret target behavior during detection, tracking, and discrimination
Develop signature simulation capabilities that capture realistic missile launch dynamics, plume characteristics, and time-varying observables that drive sensor performance
Design and generate synthetic training datasets for AI/ML systems used in sensor fusion, target discrimination, and battle management, ensuring both target-present and target-absent scenarios to reduce algorithmic bias
Integrate threat models and sensor simulations into larger operational frameworks using tools like STK and AFSIM, supporting kill chain analysis from detection through engagement
Collaborate with subject matter experts in signature characterization, sensor physics, and threat modeling to ensure simulation accuracy and technical credibility with government customers
Deliver technical briefings and documentation to stakeholders ranging from engineering teams to program leadership, translating complex modeling decisions into clear operational insights

The Impact You Will Have

Your simulation environments will inform acquisition decisions for missile defense systems that protect national security infrastructure
The synthetic datasets you generate will train machine learning models that improve threat detection and discrimination accuracy in operational systems
Your radar and signature modeling expertise will help customers understand sensor performance trade-offs before committing to hardware development
The fidelity of your threat models will enable more realistic testing of battle management algorithms, reducing risk in system integration
Your ability to represent complex multi-domain sensing scenarios will accelerate Digital Mission Engineering adoption across defense programs
The technical guidance you provide will shape how customers architect next-generation air defense systems
Your work will directly support the development of AI-driven decision support tools used in time-critical defense scenarios

What You'll Need

BS in Engineering, Physics, Computer Science, or related field with 10+ years of professional experience, or MS with 8+ years
US citizen with an active DoD TS/SCI clearance
Deep experience in air and missile defense systems, including understanding of sensor architectures, kill chain operations, and battle management concepts
Strong background in sensor phenomenology and signature modeling, particularly RF and IR signatures across missile flight phases
Proficiency in MATLAB or Python for building models, scripting simulations, and interfacing with DoD analysis frameworks
Experience with radar sensing environments and understanding of how radar bands affect target detection and characterization
Familiarity with Systems Tool Kit (STK) and Advanced Framework for Simulation, Integration and Modeling (AFSIM) is a strong plus

Who You Are

You can sit in a customer meeting, hear a vague requirement about 'better discrimination,' and walk out with a modeling plan that addresses the actual physics problem underneath
You have debugged enough radar models to know when a simulation result looks wrong, and you dig into the phenomenology until you find out why
You are comfortable presenting technical trade-offs to senior government stakeholders without oversimplifying the physics or losing them in the math
You manage multiple modeling efforts simultaneously, keeping track of which customer needs trajectory fidelity, which needs signature accuracy, and which needs both
You think about how the data you generate today will be used six months from now to train an ML model, and you build in the edge cases and variability that make those models actually work
You can work across domains, talking sensor physics with one team, machine learning data prep with another, and battle management logic with a third, without losing the thread of what you are building

The Team You'll Be Part Of Your recruiter will share more about the team structure and mission during the interview process.

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