Senior Applied Scientist, DSP
Gridware
full-time
Posted on:
Location Type: Hybrid
Location: San Francisco • California • United States
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Salary
💰 $170,000 - $205,000 per year
Job Level
About the role
- Execute end-to-end ML workflows, including exploratory data analysis, feature engineering, model training, evaluation, and optimization.
- Design and evaluate machine learning and DSP algorithms that meet strict power, memory, and latency constraints on embedded hardware.
- Conduct research and literature reviews on edge ML, resource-constrained inference, and efficient training techniques.
- Partner closely with hardware, firmware, and product teams to ensure seamless integration of models into the full system.
Requirements
- MS or PhD in Computer Science, Electrical Engineering, or a related technical field.
- 3+ years of experience developing and deploying production ML models.
- 3+ years of applied research experience in ML, DSP, or algorithm development.
- Hands-on experience working with physical sensors and modeling time-series data.
- Strong foundation in ML architectures, DSP theory, and algorithm design for real-world systems.
Benefits
- Health, Dental & Vision (Gold and Platinum with some providers plans fully covered)
- Paid parental leave
- Alternating day off (every other Monday)
- “Off the Grid”, a two week per year paid break for all employees.
- Commuter allowance
- Company-paid training
Applicant Tracking System Keywords
Tip: use these terms in your resume and cover letter to boost ATS matches.
Hard skills
machine learningDSP algorithmsfeature engineeringmodel trainingmodel evaluationmodel optimizationexploratory data analysisalgorithm developmenttime-series data modelingresource-constrained inference
Certifications
MS in Computer SciencePhD in Computer ScienceMS in Electrical EngineeringPhD in Electrical Engineering