Signal Processing Engineer

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

Bonuses for Technical Excellence and Growth

Fully Paid Insurance (Dental, Vision, Life, Disability)

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:

• Bachelor of Science degree 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