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Algorithms Team Lead
Not specifiedFull-timeAlgorithm ResearchGeospatial Technology
Lead an algorithms team responsible for processing real-world data from multiple sensor types, from raw signals through reconstruction and target characterization. Combine hands-on algorithm development with technical leadership, prioritization, and people management.
Responsibilities
- Set an algorithm roadmap with measurable goals and timelines based on business needs, customer requirements, and field constraints.
- Prioritize parallel workstreams, stop directions that are not progressing, and reallocate resources.
- Protect team focus and manage expectations around requests outside the roadmap.
- Manage team members, including goal-setting, feedback, development, hiring, and onboarding.
- Establish validation methods, performance metrics, and dataset and ground-truth management.
- Maintain output quality through technical reviews, engineering standards, reproducibility, and documentation.
- Report status, risks, and decisions to management.
- Independently develop and prototype algorithms for challenging problems.
- Analyze data, investigate anomalies and failure cases, and understand signal behavior.
- Work with field teams, including visiting field sites as needed, to understand noise sources and physical constraints.
- Review the team's work in technical depth.
Requirements
- Proven experience developing signal-processing algorithms using real physical data, not simulation alone.
- 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.
- Advanced Python skills and experience with scientific computing libraries such as NumPy and SciPy; able 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, non-destructive testing, seismology, or acoustics.
- Experience applying machine learning or deep learning to signals and imaging, with sound judgment about when classical methods are more appropriate.
- Experience moving algorithms from research and development into production.
- Familiarity with physical models and forward modeling.