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Mid Level Machine Learning Teaching Jobs in California

TEKsystems

Machine Learning Engineer

San Diego, CA · Hybrid

$70 - $95/hr

Production Machine Learning Deployments * Model Monitoring, Observability, and Optimization ... Mid-Level Note: * This posting is for an existing vacancy. Company Benefits Eligibility ...

Reddit

Machine Learning Engineer

San Francisco, CA · On-site +1

$187K - $260K/yr

Design, build, and deploy industrial-level machine learning models to solve critical problems in ad ranking, bidding, and optimization. Take full ownership of the ML lifecycle, from ideation 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

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 ...

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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, 75% Full Time, 21% Part Time, 2% Temporary, and 1% Contract. Highlights an 89% Physical, 1% Hybrid, and 10% Remote job distribution.
Mid- level Machine Learning Engineer

Mid- level Machine Learning Engineer

InstantServe LLC

San Jose, CA • On-site

Full-time

This job post has expired today. Applications are no longer accepted.

Job description

Job Title: Senior Machine Learning Engineer
Client: TetraMem Inc
Location: San Jose, California
Senior Machine Learning Engineer
About the job
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.
Requirements
  • 5+ years of relevant industry experience (or a PhD) in Computer Science, Electrical Engineering, Machine Learning, or related fields.
  • Must have prior experience managing a team, serving in a Team Lead role, or demonstrating strong technical leadership and cross-functional coordination capabilities.
  • Strong hands-on experience in machine learning, with a focus on edge AI, on-device inference, and deploying lightweight models on resource-constrained devices.
  • Expertise in modern ML frameworks such as PyTorch, TensorFlow (including TensorFlow Lite), and JAX.
  • Proficiency in Python and C/C++, with practical experience in ML model optimization and production deployment.
  • Deep experience with model quantization (PTQ/QAT), pruning, knowledge distillation, sparsity, and other compression techniques for efficient edge inference.
  • Hands-on experience developing for or integrating with AI chip SDKs, neural accelerators (NPUs/DSPs), or hardware-specific toolchains (e.g., NVIDIA TensorRT, Qualcomm Neural Processing SDK, ARM Ethos, or similar).
  • Familiarity with edge inference runtimes (ONNX Runtime, ExecuTorch, TVM) and optimizing models for hardware constraints (latency, memory footprint, power consumption).
Experience in one or more of the following areas considered a strong plus:
  • 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.
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About InstantServe

Sourced by ZipRecruiter

InstantServe provides a one-stop solution to all Healthcare, IT/Non-IT Staffing needs. Established in 2016, InstantServe is a strong workforce of over 100+ go-getters with a demonstrated background in IT/Non-IT service. We are a nationally certified SBE from the Department of Administration (State of PA). As a proud Minority Woman Owned Small Business Enterprise (M/WBE), InstantServe boasts of a strong team of professionals who have extensive experience catering to several Federal, Public, Commercial, and Healthcare Clients which includes 26 States and 46 government agencies. InstantServe is a client-centric organization that offers cost-effective and reliable solutions. Client satisfaction is sacrosanct! Our team strives to provide the best staffing and IT solutions to take your business to the next level.

Industry

Recruiting and staffing services

Company size

11 - 50 Employees

Headquarters location

Wayne, PA, US

Year founded

2016

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