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Senior Research Engineer
Santa Clara, CA (On-site)
THE POSITION:

• This is an opportunity to work on cutting-edge research and transition innovative algorithms into production systems operating at national scale. This is an opportunity to solve challenging real-world engineering problems while contributing to advanced wireless positioning technologies used in mission-critical environments. You'll work alongside a highly technical team, influence the direction of future products, and help develop innovative solutions that operate at scale
• Solve some of the most challenging problems in wireless geolocation and location intelligence with advanced software-based location technologies used in public safety, national security, telecommunications, and commercial applications worldwide by leveraging AI, machine learning, and large-scale analytics to transform location data into actionable intelligence
• Work on complex wireless geolocation problems involving incomplete, noisy, and often contradictory data collected from real-world cellular networks
• This role combines estimation theory, RF propagation, statistical inference, and large-scale data analysis. You will design algorithms that perform reliably under real-world conditions—not idealized laboratory environments—and help transition research into production systems supporting mission-critical applications
• Many of the challenges you'll tackle involve limited or imperfect ground truth, requiring creativity, mathematical rigor, and practical engineering judgment

Example Projects:

• Estimate device location using incomplete RF measurements (such as RSRP and Timing Advance) in dense urban environments
• Fuse multiple heterogeneous data sources to improve positioning accuracy and reliability
• Develop probabilistic models that quantify uncertainty and confidence in location estimates
• Extend terrestrial positioning techniques to hybrid terrestrial and Non-Terrestrial Network (NTN) systems

RESPONSIBILITIES:

• Lead the development of estimation algorithms for wireless geolocation using real-world measurement data
• Formulate and solve complex inference problems using Bayesian estimation, filtering, optimization, and related statistical techniques
• Prototype, evaluate, and refine algorithms using large-scale operational datasets
• Design validation methodologies to measure accuracy, robustness, and failure modes
• Collaborate closely with software engineering teams to transition research into production
• Contribute to architectural decisions involving accuracy, latency, scalability, and performance tradeoffs
• Present technical findings to both engineering teams and executive leadership
• Identify high-impact research opportunities aligned with product and business objectives

KNOWLEDGE, SKILLS, & EXPERIENCE REQUIRED:

• PhD in Electrical Engineering, Computer Science, Applied Mathematics, Statistics, or a related field, or equivalent experience solving complex estimation and inference problems
• Strong background in estimation theory is required such as: Least Squares, Maximum Likelihood Estimation, Kalman Filtering, Bayesian Methods
• Solid understanding of probability, statistics, and optimization
• Knowledge of RF propagation, wireless measurements, and cellular network behavior
• Strong programming skills in Python and/or MATLAB required
• Machine Learning, experience using AI-assisted tools to accelerate coding, research, and data analysis such as TensorFlow, PyTorch, Scikit-Learn, or others
• Familiarity applying AI and machine learning techniques to practical engineering challenges

Preferred Experience:

• Experience developing algorithms for real-world systems where data is noisy, sparse, or biased
• Proven ability to take technical concepts from formulation through implementation and validation
• Experience working with large datasets and evaluating model performance under operational conditions
• Telecommunications or wireless networking
• 3GPP standards
• Wireless positioning or geolocation systems
• Geolocation methods such as trilateration, fingerprinting, or sensor fusion
• Non-Terrestrial Networks (NTN), including LEO satellite positioning, RTT-based methods, ephemeris modeling, or hybrid terrestrial/non-terrestrial architectures
• Comfortable working in ambiguous environments with limited ground truth
• Able to balance theoretical rigor with practical engineering constraints
• Strong written and verbal communication skills
• Self-motivated with the ability to independently drive research initiatives
• Passion for solving difficult technical problems and building systems that perform reliably in real-world conditions

Local candidate or relocation is required; this position is on-site.
Must be authorized to work for any employer.
We are an equal opportunity employer, and we are an organization that values diversity. We welcome applications from all qualified candidates, including minorities and persons with disabilities.
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