Staff Advanced Concepts Optimization Engineer

Archer is an aerospace company based in San Jose, California building an all-electric vertical takeoff and landing aircraft with a mission to advance the benefits of sustainable air mobility. We are designing, manufacturing, and operating an all-electric aircraft that can carry four passengers while producing minimal noise. We are seeking driven, experienced, and innovative modeling, simulation, and optimization engineers to join our Concept Development team and architect the future of electric flight.

Responsibilities

Develop Archer’s multi-disciplinary optimization (MDO) engine, workflows, and high performance computing resources for aircraft concept development; Translate business goals and use cases into optimization objectives and constraints; Deploy the MDO engine to create optimal vehicle designs for use cases; Communicate findings to stakeholders and support executive decision making; Support systems engineers to transform their designs into high level requirements, and the broader Archer engineering team to turn it into reality

Requirements

Senior Level: 5+ years of relevant engineering experience; Staff Level: 8+ years of relevant engineering experience, including a proven track record of architectural ownership and technical leadership; Senior Staff Level: 12+ years of relevant engineering experience, with a history of driving multi-year technical strategy and cross-organizational impact

Nice to have

General aircraft concept design; Development of Analysis of Advanced Air Mobility (AAM) network simulations and evaluation of fleet performance; Design of eVTOL mission profiles, assessment of vehicle performance, identification of load cases; Understanding of eVTOL vehicle control algorithms; Physics-based and semi-empirical modeling of electric powertrains, sizing of electrical powertrains; Coupled aerodynamic and structural modeling and optimization of fuselage, wing, and propeller via both high-fidelity (i.e. CFD, FEA) and reduced-order methods; Development of physics-based mass and cost estimation relationships (CERs) for aerospace components; Surrogate modeling via Neural Networks or Gaussian Processes (Kriging) for use as fast-running model proxies; Modeling complex multi-physics systems of ODEs and DAEs and solving them using numerical methods; Gradient-based optimization, including Automatic Differentiation and Adjoint methods; Direct collocation and simultaneous sizing/trajectory optimization; Evolutionary algorithms and/or other non-gradient based methods for discrete-variable optimization; Uncertainty Quantification (UQ) and sensitivity analysis methods; Deploying parallelized simulation, analysis, or optimization workflows on cloud infrastructure and systems to store, query, and handle the resultant large datasets; Collaborating with discipline experts to build accurate models and validate analysis and optimization results; Communicating complex technical topics and driving consensus amongst both technical and non-technical stakeholders; Proficient in Python with NumPy, SciPy, OpenMDAO, JAX, CasADi, SMT, PyTorch, and / or other specialized scientific and optimization packages

Benefits

bonus; equity