Process Simulate Jobs in Palmdale, CA (NOW HIRING)

The Seeker Team builds the eyes and the targeting logic of our weapon system: a low-cost, mass-producible, multimodal missile seeker that leans on commercial supply chains where they outperform, and vertically integrates wherever possible. On a modern missile, the seeker is 40 to 60% of unit cost and often drives overly long delivery cycles; accordingly, the seeker team is at the tip of the spear for delivering capability to our customers and value to the taxpayer.

Seeker design has exactly two real constraints: physics and mission. Nothing else is a show-stopper, only an obstacle to overcome. We are a small, deeply technical team that drives capability through ingenuity and bias for action and ships hardware that flies. This is a wear-many-hats environment where you must be comfortable "building the airplane in flight" and stepping well outside your comfort zone; a narrowly scoped role with clean handoffs is not what this team offers.

This is an AI-native team. Through fluent use of cutting-edge agentic coding tools, one engineer here consistently out-delivers a much larger conventional team. You will have opinions about where these tools help and where they do not, provide governance input, and build out an AI-centric workflow from day one.

You will be the responsible engineer for the synthetic-data pipeline and the end-to-end performance model that drive our seeker program. This is a hybrid role: half ML data engineer, half traditional defense modeling and simulation engineer. Your top priority is the production, validation, and management of training data the algorithms team trusts. Behind the pipeline sits the classical M&S work: scene generation, atmospheric propagation, optics, focal-plane response, signal chain, target signatures, and plume/exhaust phenomenology, that gives the imagery and the predictions their physics.

You will play an integral role in standing up the team's end-to-end, real-time, physics-correct engagement simulation pipeline. You report to the seeker lead, who carries final technical authority, and partner closely with the seeker hardware, algorithm, and GNC engineers. Your job is to make sure the trades, predictions, and datasets behind every design decision are rigorous, reproducible, and trusted.

• Produce, validate, and manage the synthetic and semi-synthetic image datasets the algorithms team trains and evaluates against, with disciplined provenance, ground truth, and metadata. This is the highest-priority output of the role.
• Build and maintain end-to-end seeker performance models spanning scene, atmosphere, optics, FPA, ROIC, ADC, signal chain, and image processing in MWIR and LWIR.
• Build and validate signature models for target hardbody and plume/exhaust phenomenology.
• Stand up and own the team's real-time, physics-correct engagement simulation pipeline integrating scene, sensor, signal chain, and engagement logic.
• Integrate DTED and other georeferenced terrain data into scene generation, with discipline around frames, projections, and accuracy budgets.
• Run CPU and GPU compute at scale on cloud infrastructure to render imagery and produce datasets at the volumes the algorithms team needs.
• Author CUDA-accelerated rendering and signal-chain kernels where simulation throughput demands it; profile and optimize end-to-end pipeline throughput on multi-GPU rigs.
• Run Monte Carlo trade studies on optical, sensor, and signal-chain parameters (FPA choice, integration time, f-number, FOV, NETD/NEI) and translate results into program-level design decisions.
• Validate models against captured imagery from lab characterization, ground tests, and flight tests; close the loop on model fidelity over time.

• B.S. in Optical Engineering, Physics, EE, CS, or related; M.S. or Ph.D. preferred.
• 4 to 7 years of relevant experience, with at least 2 years on synthetic/semi-synthetic training data for ML or signal-processing algorithms, and at least 2 years of classical EO/IR modeling and simulation.
• First-hand MWIR/LWIR phenomenology: target signatures, atmospheric propagation, plume/exhaust radiometry, and scene background characterization.
• Demonstrated responsibility for synthetic image datasets used by an algorithms team to train, evaluate, and test detection, acquisition, or tracking models.
• Modern Python for science, engineering, and ML; comfortable shipping engineering software, not just notebooks.
• Hands-on with one or more of MODTRAN, MUSES, DIRSIG, or an equivalent scene/atmosphere toolchain.
• CUDA and GPU programming proficiency: writing, profiling, and optimizing kernels; reasoning about memory layout, occupancy, streams, and host-device transfer cost. You can take a slow simulation pipeline and make it fast.
• Managing datasets at scale and running CPU/GPU compute jobs on cloud infrastructure (AWS, GCP, or Azure).
• Software practices: Git, readable documentation, reviewable code, repeatable runs, and unit/E2E testing.
• Hands-on with agentic coding tools (Claude Code, Codex, OpenCode, Kilo, or similar): building, supervising, and reviewing AI-agent output to accelerate engineering work, with concrete examples of what you've shipped.

• Unreal Engine or Unity for synthetic data generation, simulation, or visualization.
• RF or radar M&S for multimodal sensor-fusion programs.
• Hardware-in-the-loop or scene-projection (DMD, IRSP) experience.
• Image processing and computer vision, including deep-learning detection and tracking.
• Familiarity with Zemax or Code V; enough to consume an optical design directly into your model.
• Hands-on with NV-IPM (formerly NVTherm/NVThermIP) or equivalent sensor performance modeling.
• Direct missile, munition, seeker, or guidance program experience.
• NeRFs and/or Gaussian Splatting.

Eligibility & Logistics Location: On-site at our Los Angeles, CA HQ; remote work is not available. Monthly weekend travel for test events and supplier engagements. Clearance: A clearance is not required for this position. Must be a U.S. Person.

Equal Opportunity & Export Compliance Furientis is an equal-opportunity employer. To comply with U.S. export control laws, employment is contingent on eligibility to access export-controlled information.