Signal Analysis Engineer III

NASA Jet Propulsion Laboratory

1d • $142,792 - $175,760

About The Position

JPL is seeking a Signal Analysis Engineer III/Algorithm Developer to join the Multi-sensor Worldwide Ocean Winds (MWOW) team. MWOW combines ocean surface wind retrievals from satellite scatterometers, radiometers, and synthetic aperture radars into a coregistered, globally gridded product for operational and research users, with data distributed publicly through NASA's Physical Oceanography Distributed Active Archive Center (PO.DAAC). The project is developing and delivering successive product versions, including a historical reprocessing back to 1999 and planned wind stress and wind derivative products. The role focuses on developing retrieval, correction, harmonization, and gap-filling algorithms—many using machine learning—and turning research algorithms into tested, documented, open-source software suitable for operational production.

The successful applicant will work at the intersection of ocean microwave remote sensing and modern machine learning. They will develop algorithms and ground-processing software for MWOW Level 3 ocean surface wind products, develop tools for training and validation, collaborate with an international team, and document algorithms and communicate results to project stakeholders and the scientific community. The position reports administratively to the Group Supervisor of Group 363L and programmatically to the MWOW Task Manager. JPL is located in Pasadena, California; the posting describes its campus-like work environment but does not specify whether this role is onsite, hybrid, or remote.

Requirements

• Bachelor’s degree in Electrical Engineering, Geophysics, Physics, Applied Mathematics, Computer Science, or a related discipline and at least 6 years of related work experience; or a related master’s degree and at least 4 years; or a related Ph.D. and at least 2 years.
• Understanding of ocean radar and radiometer remote sensing, such as scatterometry, synthetic aperture radar, radar altimetry, or passive microwave radiometry, including retrieval of geophysical parameters from backscatter or brightness temperature.
• Basic understanding of ocean surface winds and their relationship to atmospheric and oceanic processes.
• Demonstrated ability to develop, train, and validate machine-learning models for geophysical retrieval, regression, or image-to-image problems using a standard framework such as TensorFlow or PyTorch.
• Strong Python development skills, including experience with the scientific stack (NumPy, SciPy, xarray, and netCDF4), and collaborative software development using Git and GitHub.
• Data-analysis skills, including experience with large swath and gridded geophysical datasets.
• Strong communication skills.
• Ability to work well in a team environment.

Nice To Haves

• Experience with deep-learning architectures for super-resolution, gap filling, and data fusion, such as neural operators, score-based diffusion models, state-space and transformer architectures, or physics-constrained learned solvers such as 4DVarNet and physics-informed neural operators.
• Hands-on experience retrieving ocean surface winds from scatterometer, SAR, or passive microwave observations, or retrieving sea-ice concentration and extent.
• Experience collaboratively developing and delivering open-source software products, including user documentation, unit tests, and releases.
• Familiarity with sea surface height and ocean surface current estimation from wide-swath and nadir altimetry.
• Experience with NetCDF-4, CF and ACDD metadata conventions, and publishing data products through a NASA DAAC.
• Experience with cloud computing or high-performance computing applied to large-scale scientific data reprocessing.
• Expertise with Python and a compiled language such as C++ or Fortran.

Responsibilities

• Design, implement, test, and validate algorithms and ground-processing software for MWOW Level 3 ocean surface wind products distributed to stakeholders and the public through PO.DAAC.
• Develop algorithms that may include sea-ice concentration estimation and wind correction near the ice edge; wind retrieval from Ka-band and L-band radar; wind-direction retrieval from speed-only SAR and passive microwave observations; coastal land-contamination correction; Ku-band scatterometer rain correction; cross-sensor speed harmonization; triple-collocation uncertainty models; and quality control and regridding of the merged product.
• Apply machine learning to improve effective wind-product resolution near coasts, sea ice, and over the open ocean, and to fill spatial and temporal gaps between satellite overpasses using collocations with high-resolution SAR observations.
• Develop collocation, simulation, and analysis tools to train and validate algorithms, and characterize their performance against buoy networks, the ASCAT reference standard, and ERA-5 reanalysis.
• Deliver tested, documented, open-source Python software, collaborating in existing MWOW repositories through GitHub feature branches, pull requests, and code review.
• Collaborate with international partners, including KNMI, Remote Sensing Systems, PO.DAAC, and the NASA Ocean Vector Winds Science Team, to develop, test, and deliver algorithms and software systems.
• Apply principles and techniques in microwave remote sensing, radar and radiometer signal processing, and machine learning.
• Document algorithms in the MWOW Algorithm Theoretical Basis Document, support project milestone reviews, and communicate results to agency stakeholders and the scientific community.

Benefits

• Health, dental, vision, wellbeing, and retirement plans.
• Paid time off.
• Learning benefits.
• Rideshare benefits.
• Childcare.
• Flexible schedule.
• Parental leave.
• Other traditional and unique benefits and perks; benefits for eligible positions are effective the first day of the month coincident with or immediately following the employee’s start date.

What This Job Offers

• Job Type: Full-time
• Career Level: Senior