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Deep Learning Intern Jobs in Berkeley, CA (NOW HIRING)

pony.ai

Research Intern - Deep Learning

Fremont, CA · On-site

$7.0K - $10K/mo

Develop and deploy deep learning models, including vision language models (VLMs) and Large Language Models (LLMs) * Design and implement multi-modality and multi-task perception models focusing on 3D ...

pony.ai

Research Intern - Deep Learning

Fremont, CA · On-site

$7.0K - $10K/mo

Develop and deploy deep learning models, including vision language models (VLMs) and Large Language Models (LLMs) * Design and implement multi-modality and multi-task perception models focusing on 3D ...

pony.ai

Research Intern - Deep Learning

Fremont, CA · On-site

$7.0K - $10K/mo

Develop and deploy deep learning models, including vision language models (VLMs) and Large Language Models (LLMs) * Design and implement multi-modality and multi-task perception models focusing on 3D ...

Crowe LLP

Machine Learning Intern

San Francisco, CA · On-site

$27 - $42/hr

Anomaly detection using deep neural networks Numerical optimization applied to problems in ... To that end, there are three major components with which an intern should expect to engage.

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How much do deep learning intern jobs pay per hour?

As of Sep 14, 2026, the average hourly pay for deep learning intern in Berkeley, CA is $20.86, according to ZipRecruiter salary data. Most workers in this role earn between $17.64 and $23.56 per hour, depending on experience, location, and employer.

What does a deep learning intern do?

A Deep Learning Intern typically assists with designing, developing, and testing deep learning models under the supervision of experienced machine learning engineers or researchers. Their tasks may involve data preprocessing, model training, evaluation, and implementing neural network architectures for tasks like image recognition, natural language processing, or other AI applications. Interns often help with literature reviews, experiment tracking, and preparing reports or presentations of their findings. This role provides hands-on experience in working with state-of-the-art machine learning frameworks such as TensorFlow or PyTorch.

What skills and qualifications are needed to thrive as a deep learning intern?

To thrive as a Deep Learning Intern, you need a solid background in mathematics, programming (especially Python), and foundational knowledge of machine learning concepts, often backed by coursework or relevant projects. Familiarity with frameworks like TensorFlow or PyTorch, as well as experience using version control systems like Git, are typically required. Strong problem-solving abilities, curiosity, and effective communication skills help interns collaborate and learn quickly in a dynamic research environment. These skills and qualities are essential for contributing meaningfully to cutting-edge AI projects and rapidly adapting to evolving technologies.

What types of projects can a deep learning intern expect to work on, and how is mentorship typically structured?

As a Deep Learning Intern, you can expect to work on projects such as developing and training neural network models, data preprocessing, and conducting experiments to improve model accuracy. Interns are often integrated into small teams where they collaborate closely with experienced machine learning engineers and researchers. Mentorship is usually structured through regular check-ins, code reviews, and collaborative problem-solving sessions, giving interns the opportunity to learn industry best practices and receive feedback on their work. This setup provides a supportive environment for skill development and hands-on experience with real-world deep learning challenges.

What job categories do people searching Deep Learning Intern jobs in Berkeley, CA look for?

The top searched job categories for Deep Learning Intern jobs in Berkeley, CA are:

What cities near Berkeley, CA are hiring for Deep Learning Intern jobs?

Cities near Berkeley, CA with the most Deep Learning Intern job openings:

Deep Learning Intern jobs near you
Infographic showing various Deep Learning Intern job openings in Berkeley, CA as of August 2026, with employment types broken down into 1% As Needed, 73% Full Time, 23% Part Time, 1% Temporary, and 2% Contract. Highlights an 87% Physical, 2% Hybrid, and 11% Remote job distribution, with an average salary of $43,389 per year, or $20.9 per hour.

Machine Learning Intern - KWS/AED

Redwood City, CA • On-site

Syntiant
51 - 200 employees
Learn more about Syntiantexternal

Temporary

Posted 6 days ago

Job description

Summary Description:

Syntiant Corp., a leader in the high-growth AI software and semiconductor solutions space, is looking for a Machine Learning Intern to take on a critical role supporting our Algorithms team's work on keyword spotting (KWS) and audio event detection (AED) models deployed on ultra-low-power edge hardware.

The Machine Learning Intern will work alongside senior ML engineers to help build, evaluate, and improve deep learning models that run directly on Syntiant's NDP-class neural decision processors - models that must detect wake words, spoken commands, and acoustic events (e.g., glass breaking, alarms, sirens) in real time under extremely tight memory and power budgets. This role spans the full modeling pipeline, from signal processing and data curation through architecture design, training, and evaluation against hardware constraints.

Requirements

Specific Duties and Responsibilities:

  • Support development and evaluation of KWS and AED models, including single-stage and cascaded (multi-stage gate/verifier) detection architectures.
  • Assist with audio pipeline and feature extraction work - filterbank design, log-mel and PCEN-based frontends, and diagnosing numerical or performance issues in training/eval pipelines.
  • Help design and prune CNN architectures to fit hardware constraints (fixed input shapes, 8-bit quantization, limited parameter budgets, restricted op sets such as depthwise separable convolutions with hardware-supported stride/pooling operations).
  • Build and run false-accept (FA) diagnostic tooling - categorized probe sets, confusion analysis, Grad-CAM/occlusion-style visualization to understand what a model is actually keying on.
  • Contribute to hard-negative mining and data augmentation strategies (e.g., SNR-based background noise mixing, targeted negative class collection) to reduce false accepts across everyday household/environmental sounds.
  • Help plan and track data collection efforts, including structuring datasets by acoustic category/spec and maintaining collection logs and inventories.
  • Analyze model run results across experiment variants (architecture, data, frontend) and summarize findings for the team.
  • Collaborate with ML, DSP, and hardware/firmware engineers to validate models against real deployment conditions.

Qualifications, Education, and Experience Required:

  • Candidate pursuing or has completed a Bachelor's or Master's degree in Computer Science, Electrical Engineering, Machine Learning, or a related field, with hands-on experience in deep learning for audio or speech (coursework, research, or project experience with CNNs/RNNs on spectrogram or time-series audio data).
  • Proficiency in Python and a deep learning framework (TensorFlow/Keras preferred; PyTorch acceptable).
  • Familiarity with audio signal processing fundamentals (spectrograms, mel filterbanks, feature extraction).
  • Understanding of standard ML evaluation concepts (precision/recall trade-offs, ROC/DET curves, confusion analysis) - bonus if applied to detection/verification tasks rather than pure classification.
  • Exposure to model efficiency concepts (quantization, parameter budgets, edge/embedded ML constraints) is a strong plus, though not required.
  • Strong analytical mindset, comfort working with messy real-world data, and clear written communication for summarizing experimental results.
  • Prior internship, research, or personal project experience in audio ML, KWS, or acoustic event detection is a plus but not required.

Benefits

About Syntiant:

Founded in 2017 and headquartered in Irvine, Calif., Syntiant Corp. is a leader in delivering hardware and software solutions for edge AI deployment. The company's purpose-built silicon and hardware-agnostic models are being deployed globally to power edge AI speech, audio, sensor and vision applications across a wide range of consumer and industrial use cases, from earbuds to automobiles. Syntiant's advanced chip solutions merge deep learning with semiconductor design to produce ultra-low-power, high performance, deep neural network processors. Syntiant also provides compute-efficient software solutions with proprietary model architectures that enable world-leading inference speed and minimized memory footprint across a broad range of processors. The company is backed by several of the world's leading strategic and financial investors including Intel Capital, Microsoft's M12, Applied Ventures, Bosch Ventures, the Amazon Alexa Fund, and Atlantic Bridge Capital. More information on the company can be found by visiting www.syntiant.com.

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