Qualifying candidate will be a self starter serving as a subject matter expert in Digital Signal Processing (DSP) and Algorithmic Development. Desired experience includes X-Midas, Python, GNU-Radio, Julia, or MATLAB development skills. Familiarity with DSP Techniques (such as Cyclo-Stationary processing, Cumulants, CAFs, Polyphase Filterbanks, etc), Radar Processing, Wireless Communications, Artificial Intelligence & Machine Learning, and/or Satellite Engineering. Individual should be comfortable integrating algorithms and processing data from receiving hardware systems.
An exceptional technical background with strong mathematical and software skills to support signal processing developments that can transition quickly into mission operations is essential. Candidate must possess exceptional knowledge in DSP techniques and algorithms, strong mathematics background, ability to write fluent software.
SATSS Benefits Include:
25% Retirement Contribution ( SATSS contributes an amount equal to 25% of your salary to retirement)
40 Days of Paid Time Off per Year
Gym Reimbursement
College 529 Match
Equity
100% Company Paid Healthcare or Cash in Lieu of Coverage
Paid for Conferences, Journals, Publications, Radio Hardware, Training, and Textbooks
Employee Owned (Options issued to our entire Team!)
Must Have, U.S. Security Clearance
TS/SCI
Must Have, Digital Signal Processing Expertise
Familiarity with processing and exploitation of signals collected on a variety of SDR platforms
Working knowledge of Radar and/or Wireless Communication Signals
Nice to have Experience includes real-world development of various Modulation Types, FECs, Packetized Bit Structure, Beamforming, PLLs, and/or Matched Filters, SAR/ISAR Image Formation, Range Doppler Processing, and related concepts.
Development of DSP algorithms for emitters operating in dynamic & complex RF environments
Self-starter comfortable with Customer interactions and high level technical direction
Ability to support Customer development needs with rapid prototyping
Dynamic environment requires ability to pivot to new technical solutions in short order to meet Customer requirements and/or mission need
Must be able to implement technical solutions in software
C/C++, Python, Julia in a Linux development environment. Basic understanding of Fortran, Java, Perl, JavaScript, XML, JSON, and other languages will aid in development
Familiarity with either X-Midas or MATLAB || OCTAVE, GNUradio, and LAPACK || LINPACK software suites are strongly desired
Previous use of code management such as git, gitlab, svn, mercurial, or other code repositories is desired with a basic understanding of cloning, branches, merges, unit testing, continuous integration, automated build and test cycle (jenkins, pipelines, etc), and automated code checkout.
Ability to make library calls across languages. Familiarity with Cython, SWIG, and other Pythonic/C wrappers with cross-compiler support is desired.
Experience writing multi-threaded software using CPUs in a Linux server environment
Comfortable with GPU and CUDA related processing capabilities
Strong grasp of Linux commands and ability to write shell scripts.
Nice to Have, knowledge of Linear Algebra, Physics, and Statistical Mathematics.
Strong focus on matrix methods (eigen/QR decomp, components analysis), higher order derivatives (Jacobians, Hessians, and traces thereof), robust estimators, gradient descent approaches, ray tracing methodologies, finite fields, set theory, polynomials, quadratics, root finding techniques (such as Newton's Method), etc.
Nice to Have, Machine Learning / Artificial Intelligence Experience
Knowledge of the underlying mathematics for AI/ML algorithms and their application to the signal processing domain and sense making
Ability to author Kernels, Density Estimators, and advanced Clustering Algorithms
Fundamental understanding of autoencoders (variational, denoising, etc), latent spaces, components analysis, sparsity approaches, regularization approaches for ill-posed problem sets, higher dimensional spaces, adaptive filters
Familiarity with GANs (Generative Adversarial Networks)
Familiarity with Python libraries such as scikit-learn, scikit-image, numpy, scipy, mayavi, matplotlib, pandas, keras, tensorflow, caffe, nltk, statsmodels, pybrain
Education Requirements:
B.S. or higher in Electrical Engineering, Physics, Mathematics, Computer Science, Computer Engineering, Aerospace Engineering, or related field.
Preference for graduate school degree with a strong track record of publications and/or presentations at community conferences