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Mid Level Machine Learning Teaching Jobs in California

Spotline

Machine Learning Engineer

Fremont, CA · On-site

P2 - Mid-Level | 3-5 years exp Requirements: In-depth expertise in Python for high-performance ... implement machine learning models using supervised learning techniques for design support and ...

Hive

Machine Learning Engineer

San Francisco, CA · On-site

$120K - $180K/yr

You have strong experience with a high-level machine learning frameworks such as Tensorflow, Caffe, or Torch, and familiarity with the others * You know the ins and outs of Python, especially as it ...

Hive

Machine Learning Engineer

San Francisco, CA

$120K - $180K/yr

You have strong experience with a high-level machine learning frameworks such as Tensorflow, Caffe, or Torch, and familiarity with the others * You know the ins and outs of Python, especially as it ...

NTENT

Machine Learning Engineer

Carlsbad, CA · On-site

Design tools and interfaces for interactive machine learning and teaching. * Research and development on cutting-edge machine learning technologies. Qualifications and Skills: * Graduate degree in ...

NTENT

Machine Learning Engineer

Carlsbad, CA · On-site

Design tools and interfaces for interactive machine learning and teaching. * Research and development on cutting-edge machine learning technologies. Qualifications and Skills: * Graduate degree in ...

Ema

Software Engineer, Machine Learning

Bodega Bay, CA · On-site

$160K - $220K/yr

At least 2 years of industry experience in building and deploying production-level machine learning models. * Deep understanding and practical experience with NLP techniques and frameworks, including ...

Ema

Software Engineer, Machine Learning

San Francisco, CA · On-site

$160K - $220K/yr

At least 2 years of industry experience in building and deploying production-level machine learning models. * Deep understanding and practical experience with NLP techniques and frameworks, including ...

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Mid Level Machine Learning Teaching information

What is a $900000 AI job?

A $900,000 AI job typically refers to high-level roles in artificial intelligence, such as senior machine learning engineers or AI research directors, often requiring advanced skills in deep learning, data analysis, and programming. These positions usually involve leadership responsibilities, extensive experience, and may include stock options or bonuses as part of compensation.

Which 3 jobs will survive AI?

Mid Level Machine Learning Teaching roles are likely to persist as they require specialized knowledge, human interaction, and the ability to adapt to new AI tools. Jobs that involve complex problem-solving, creativity, and emotional intelligence, such as data scientists, AI ethics specialists, and technical trainers, are also expected to remain in demand despite AI advancements.

Will MLE be replaced by AI?

Mid Level Machine Learning Engineers (MLEs) focus on developing, deploying, and maintaining machine learning models, which requires a combination of programming, data analysis, and domain knowledge. While AI automation tools can assist with certain tasks, MLEs are essential for designing complex models, troubleshooting, and ensuring ethical and effective implementation, making complete replacement unlikely in the near term.

What is the difference between Mid Level Machine Learning Teaching vs Data Scientist?

AspectMid Level Machine Learning TeachingData Scientist
Required CredentialsBachelor's or Master's in CS, ML, or related; teaching experienceBachelor's or Master's in CS, Data Science, or related; often requires experience
Work EnvironmentEducational institutions, online platforms, corporate trainingTech companies, finance, healthcare, research labs
Employer & Industry UsageEducational and training sectors, universities, online educationPrivate sector, industry-specific applications, research
Common Search & Comparison IntentUnderstanding teaching roles in ML, educational careersData analysis, modeling, industry applications

Mid Level Machine Learning Teaching focuses on educating students or professionals in ML concepts, often requiring teaching experience and educational credentials. Data Scientists analyze data, build models, and apply ML techniques in industry settings. While both roles involve ML knowledge, teaching emphasizes instruction, whereas data science emphasizes application and analysis.

Can I learn ML in 3 months?

For a mid-level machine learning teaching role, gaining foundational knowledge in machine learning typically requires several months of dedicated study, including understanding algorithms, programming in Python, and working with tools like scikit-learn or TensorFlow. While intensive learning over three months can build basic skills, achieving proficiency suitable for teaching or advanced roles usually takes longer and involves practical experience and project work.
What are popular job titles related to Mid Level Machine Learning Teaching jobs in California? For Mid Level Machine Learning Teaching jobs in California, the most frequently searched job titles are:
What job categories do people searching Mid Level Machine Learning Teaching jobs in California look for? The top searched job categories for Mid Level Machine Learning Teaching jobs in California are:
What cities in California are hiring for Mid Level Machine Learning Teaching jobs? Cities in California with the most Mid Level Machine Learning Teaching job openings:
Mid Level Machine Learning Teaching jobs near you
Infographic showing various Mid Level Machine Learning Teaching job openings in California as of July 2026, with employment types broken down into 1% As Needed, 71% Full Time, 25% Part Time, 1% Temporary, and 2% Contract. Highlights an 86% Physical, 2% Hybrid, and 12% Remote job distribution.

Mid-Level Machine Learning Engineer

TetraMem - Accelerate The World

San Jose, CA • On-site

Full-time

Re-posted 14 days ago

Job description

Job Summary:
TetraMem is a company focused on accelerating the world through innovative technology, and they are seeking a Mid-Level Machine Learning Engineer to develop and optimize machine learning models for edge AI applications. The role involves collaborating with hardware and software teams, providing mentorship, and researching state-of-the-art ML techniques to enhance model efficiency.
Responsibilities:
• Develop, optimize, and deploy lightweight machine learning models for edge AI applications, particularly for audio processing.
• Implement and optimize ML models on embedded platforms, including FPGA and custom ASIC solutions.
• Work closely with hardware and software teams to integrate ML models into production systems.
• Research and implement state-of-the-art ML techniques to enhance model efficiency, latency, and power consumption for embedded AI applications.
• Improve inference efficiency and model compression techniques, including quantization, pruning, and knowledge distillation.
• Collaborate with cross-functional teams to drive innovation and contribute to the overall system architecture.
• Provide technical leadership and mentorship to junior engineers.
• Publish research findings, present at conferences, and contribute to open-source projects when applicable.
Qualifications:
Required:
• 5+ years of experience or PhD in Computer Science, Electrical Engineering, or related fields.
• Strong experience in machine learning, with a focus on edge AI and lightweight model deployment.
• Expertise in ML frameworks such as PyTorch, TensorFlow, JAX.
• Proficiency in programming languages such as C/C++, Python, and experience with ML model optimization.
• Ability to work independently and collaboratively in a fast-paced startup environment.
• Ability to provide mentorship, technical guidance, and career development support to junior engineers and interns.
Preferred:
• Understanding of ML compiler and runtime design.
• Experience working with tools such as Optimum, ONNX, TensorRT, TFLite/LiteRT, ncnn, or CoreML.
• Familiarity with hardware acceleration techniques.
• Experience in embedded system development.
Company:
TetraMem is developing cutting-edge analog computing solutions for AI applications, offering exceptional performance with ultra-low power consumption. Founded in 2018, the company is headquartered in Newark, USA, with a team of 51-200 employees. The company is currently Growth Stage.

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