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Lead Algorithm Engineer
Not specifiedFull-timeAlgorithm ResearchGeospatial Technology
Lead the algorithms team within a geotechnical group, combining people leadership and hands-on development. Own the processing chain for noisy field data from multiple sensors, from raw signals to customer-facing underground maps.
Responsibilities
- Lead and develop the algorithms team, including setting individual goals, providing feedback, supporting professional development, and hiring and onboarding.
- Translate business needs, customer requirements, and field constraints into an algorithm roadmap with measurable goals and timelines.
- Set priorities across workstreams, discontinue directions that are not progressing, and protect the team’s focus.
- Define validation methods, performance metrics, and practices for dataset and ground-truth management.
- Own output quality through technical reviews, engineering standards, reproducible results, and useful documentation.
- Report status, risks, and decisions to management.
- Independently prototype and develop algorithms for complex technical problems; investigate data, failure cases, and anomalies.
- Work with field teams and, when needed, visit field sites to understand signal noise and physical constraints.
- Review team work in depth and maintain hands-on involvement in technical decisions.
Requirements
- Proven experience developing signal-processing algorithms using real physical data.
- Strong digital signal processing fundamentals, including time- and frequency-domain analysis, filtering, deconvolution, signal-to-noise ratio, and sampling.
- Experience with inverse problems, reconstruction, or imaging, including ill-posed problems and regularization.
- Full command of Python and scientific libraries such as NumPy and SciPy; ability to write clean, maintainable code.
- Prior management experience or demonstrated technical leadership of several developers and an end-to-end project.
- Ability to make decisions under uncertainty and prioritize competing directions.
- Professional-level Hebrew and English.
Nice to have
- Background in geophysics or another field involving measurement through complex media, such as medical imaging, sonar, radar, nondestructive testing, seismology, or acoustics.
- Experience applying machine learning or deep learning to signals and imaging, with sound judgment about when classical methods are preferable.
- Experience taking algorithms from research and development into production.
- Familiarity with physical models and forward modeling.