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Signal Processing Machine Learning Jobs in California

Apple

HID Algorithms Engineer

Cupertino, CA · On-site

$98K - $133K/yr

... signal processing, machine learning, etc. Experience programming in C++ and Python Able to communicate clearly and collaborate with cross-functional teams Excellent problem solving and root cause ...

Liquid XR

Machine Learning Engineer

Los Angeles, CA · On-site +1

Replace or augment classical signal processing pipelines with learned models * Design training ... Strong experience with machine learning for time-series data * Experience with Transfer learning ...

Apple

HID Algorithms Engineer

Cupertino, CA · On-site

$98K - $133K/yr

... signal processing, machine learning, etc. Experience programming in C++ and Python Able to communicate clearly and collaborate with cross-functional teams Excellent problem solving and root cause ...

nudge

Machine Learning Engineer

San Francisco, CA · On-site

About the role As a Machine Learning Lead at Nudge, you will drive the development of next-generation ML and imaging systems at the intersection of ultrasound, signal processing, and neuroscience.

Nudge

Machine Learning Engineer

San Francisco, CA · On-site

About the role As a Machine Learning Lead at Nudge, you will drive the development of next-generation ML and imaging systems at the intersection of ultrasound, signal processing, and neuroscience.

Masimo

Sr Engineer, AI/Machine Learning

Irvine, CA · On-site

$140K - $170K/yr

Develop new advanced algorithms using, machine learning techniques, deep learning models, digital signal processing techniques, optimization and numerical modeling in MATLAB, Python or similar ...

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Signal Processing Machine Learning information

See California salary details

$52.8K

$129.6K

$191K

How much do signal processing machine learning jobs pay per year?

As of Sep 14, 2026, the average yearly pay for signal processing machine learning in California is $129,629.00, according to ZipRecruiter salary data. Most workers in this role earn between $107,100.00 and $145,600.00 per year, depending on experience, location, and employer.

What is a signal processing machine learning?

A Signal Processing Machine Learning job involves developing algorithms that analyze and process signals (such as audio, images, video, or sensor data) using machine learning techniques. Professionals in this role apply concepts from digital signal processing (DSP) to extract meaningful patterns, enhance signal quality, and improve data-driven predictions. They work in diverse fields like telecommunications, biomedical engineering, finance, and autonomous systems. Typical tasks include feature extraction, noise reduction, and deploying deep learning models for real-time signal interpretation. Strong skills in mathematics, programming (Python, MATLAB), and frameworks like TensorFlow or PyTorch are essential.

What does a signal processing machine learning do?

As a Signal Processing Machine Learning professional, you can expect to work on projects that involve developing and optimizing algorithms for tasks such as audio or image recognition, anomaly detection, or sensor data analysis. Daily responsibilities often include pre-processing and cleaning large datasets, feature extraction, building and training machine learning models, and validating system performance. Collaboration with cross-functional teams—such as hardware engineers, data scientists, and software developers—is common to integrate your solutions into products or services. The work environment is typically dynamic and may involve both research-oriented tasks and practical implementation to create impactful, data-driven applications.

What are the key skills and qualifications needed to thrive in signal processing machine learning?

To thrive in Signal Processing Machine Learning, you need a strong background in mathematics, digital signal processing, and machine learning, generally supported by a relevant degree in electrical engineering, computer science, or a related field. Experience with programming languages such as Python or MATLAB, familiarity with frameworks like TensorFlow or PyTorch, and knowledge of signal processing libraries are typically required. Analytical thinking, problem-solving ability, and effective communication are crucial soft skills in this position. These competencies enable you to design, implement, and refine advanced algorithms that address complex, real-world data challenges.

What are popular job titles related to Signal Processing Machine Learning jobs in California?

For Signal Processing Machine Learning jobs in California, the most frequently searched job titles are:

What job categories do people searching Signal Processing Machine Learning jobs in California look for?

The top searched job categories for Signal Processing Machine Learning jobs in California are:

Signal Processing Machine Learning jobs near you
Infographic showing various Signal Processing Machine Learning job openings in California as of September 2026, with employment types broken down into 1% Internship, 1% As Needed, 73% Full Time, 22% Part Time, 1% Temporary, and 2% Contract. Highlights an 82% Physical, 2% Hybrid, and 16% Remote job distribution, with an average salary of $129,629 per year, or $62.3 per hour.

AI Engineer, Time-Series Signal Processing

Palo Alto, CA • On-site

BrightAI Corporation
Software Development • 11 - 50 employees
Learn more about BrightAI Corporationexternal

Full-time

Re-posted 15 days ago

Job description

AI Engineer, Time-Series Signal Processing
BrightAI is a high-growth Physical AI company transforming how businesses interact with the physical world through intelligent automation. Our platform processes visual, spatial, and temporal data from billions of real-world events-captured through edge devices, mobile sensors, and large-scale cloud infrastructure-to deliver intelligent, real-time decisions.
We are now hiring an AI Engineer - Time-Series Signal Processing to lead the development of AI/ML solutions built on high-frequency multi-modal sensor data. This is a critical role focused on modeling and understanding time-series signals coming from IoT devices equipped with various sensors (IMU, acoustic, pressure, temperature, etc.) that drive intelligent automation across physical infrastructure systems.
You'll work on building cutting-edge real-time AI models that process noisy, high-throughput data streams and extract meaningful insights for real-world decision-making-at both the edge and cloud scale.
Responsibilities
  • Design and implement real-time signal processing and ML pipelines for multi-modal time-series data such as those acquired from IMUs, microphones, pressure or force sensors, ultrasonic transducers, and similar sensor sources.
  • Develop and deploy ML models for time-series classification, prediction, anomaly detection, activity recognition, condition monitoring and pattern analysis.
  • Lead research and implementation of RNN-based architectures (especially LSTMs and their variants) as well as temporal transformer models as needed.
  • Build and tune classical and tree-based ML models (XGBoost, LightGBM, Random Forests, and other gradient-boosted ensembles) for time-series tasks, including feature engineering and model interpretability (e.g., SHAP).
  • Work with SCADA systems and industrial telemetry data-ingesting and modeling high-frequency, multi-channel operational data streams from physical assets.
  • Collaborate with hardware, embedded, and product teams to integrate models into edge devices and IoT platforms.
  • Drive experimentation and optimization of signal-processing techniques (e.g., filtering, feature extraction, event detection) to enhance model input quality.
  • Design and maintain scalable workflows for ingesting, labeling, training, and evaluating multi-channel time-series datasets.
  • Stay current with advances in time-series modeling, signal processing, and real-time inference, and incorporate them into product roadmaps.
  • Ensure model robustness, performance, and reliability in production environments, including edge deployments.

Educational Background
  • Degree in Electrical Engineering, Computer Science, or a related field, with a strong focus on signal processing, time-series analysis, and machine learning.
  • Strong academic or industry track record in time-series modeling, signal processing, or real-time AI systems.

Required Skills & Expertise
  • 2+ years of experience developing signal processing and ML solutions for time-series sensor data. Track record of bringing at least one ML solution to market.
  • Deep understanding of digital signal processing (DSP) methods: filtering, sampling, windowing, FFT, feature extraction, etc.
  • Hands-on experience with RNNs (especially LSTMs/GRUs) and/or temporal convolutional networks for time-series modeling.
  • Proficiency with tree-based and gradient-boosting models (XGBoost, LightGBM, Random Forests) applied to time-series and sensor data, including hyperparameter tuning and explainability.
  • Experience working with SCADA systems and industrial telemetry data (high-frequency sensor feeds, time-stamped operational data, multi-channel ingestion from physical assets).
  • Proven experience with time-series data from physical sensors such as IMUs, microphones, vibration or pressure sensors.
  • Strong coding skills in Python and fluency with ML/DL frameworks (e.g., PyTorch, TensorFlow, Keras).
  • Experience in optimizing and deploying models in real-time or near-real-time environments, including edge devices or resource-constrained embedded systems.
  • Fluency with best practices in data labeling, augmentation, and evaluation for time-series tasks.
  • Excellent problem-solving and collaboration skills with the ability to work across teams.
  • Strong communication skills with the ability to convey findings and recommendations to internal and external stakeholders.

Bonus Qualifications
  • Experience building end-to-end AI systems for structural health monitoring, condition monitoring, anomaly detection, activity recognition, or motion tracking.
  • Experience with predictive maintenance on industrial equipment using SCADA/telemetry data.
  • Familiarity with experiment tracking and model lifecycle tooling (e.g., MLflow, DVC).
  • Exposure to streaming/online inference patterns (e.g., EWMA normalization, windowed feature extraction on live data).
  • Proficiency in embedded software or deploying models to constrained environments (e.g., using TFLite, ONNX, or custom firmware).
  • Familiarity with containerized workflows and Linux-based development environments.
  • Experience with Agile workflows and tools such as JIRA, Git, and CI/CD pipelines.
  • Prior work in startup or high-pace teams with experience in building real-time systems from the ground up.
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About BrightAI

Sourced by ZipRecruiter

Industry

Software development

Company size

11 - 50 Employees

Headquarters location

San Francisco, CA, US

Year founded

2019

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