Signal Analysis Engineer III

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Located in Pasadena, California, JPL has a campus-like environment situated on 177 acres in the foothills of the San Gabriel Mountains and offers a work environment unlike any other: we inspire passion, foster innovation, build collaboration, and reward excellence.

We are looking for a Signal Analysis Engineer III/Algorithm Developer to join the Multi-sensor Worldwide Ocean Winds (MWOW ) team. MWOW is a NASA Satellite Needs Working Group (SNWG) Cycle 4 solution, selected in response to one of the top five inter-agency needs identified in the 2022 survey, and it is funded through May 2030. MWOW combines ocean surface wind retrievals from every useful satellite scatterometer, radiometer, and synthetic aperture radar into a single coregistered, globally gridded product, delivered 6-hourly on both a 0.125-degree and a 1/64-degree global grid and archived for public distribution at NASA's Physical Oceanography Distributed Active Archive Center (PO.DAAC).

Its users are the operational and research communities at NOAA and the Department of Energy, who need finer spatial resolution, better coverage near coasts and sea ice, and a consistent multi-decade record.

A Version 0.2 beta product drawing on seven satellites is available now for stakeholder evaluation. Version 1, scheduled for release in September 2027, expands the product to eight scatterometers (ASCAT-A/B/C, EOS-6, HY-2B/C, ScatSAT-1, and QuikSCAT), six radiometers (SMAP, GMI, AMSR2, and SSMI-F16/F17/F18) together with the historical TMI and SSM/I F-series, and three synthetic aperture radars (Sentinel-1, NISAR, and the SWOT Ka-band radar interferometer), and reprocesses the historical record back to 1999.

Versions 2 and 3 follow in September 2028 and September 2029, with a wind stress and wind derivative product in March 2030. Reaching those deliveries requires new retrieval and correction algorithms, and a large share of them are machine learning algorithms.

The MWOW algorithms are described in the MWOW Algorithm Theoretical Basis Document (ATBD), which lays out a ten-step per-sensor processing chain and identifies where each algorithm still has to be built. Several of the hardest remaining problems are learning problems: estimating sea-ice concentration and correcting winds near the ice edge, retrieving wind direction from sensors that measure only speed, correcting rain contamination in Ku-band back scatter, harmonizing sensors with different resolutions and calibrations onto a common scale, and filling the spatial and temporal gaps between satellite overpasses.

The coastal and near-sea-ice product plans additionally call for using machine learning to raise the effective resolution of scatterometer and radiometer retrievals by training against MWOW's own collocations with high-resolution SAR observations. The team has separately proposed a machine-learning augmentation to MWOW covering kilometer-scale super-resolution of the wind field, blending of SWOT wide-swath and conventional nadir altimeter sea surface height, and derivation of total ocean surface currents.

That augmentation is not yet funded and is not the primary emphasis of this position, but we want someone who could take a leading role in it if it is.

Signal Analysis Engineer III…