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Remote Machine Learning Engineer Jobs in Livermore, CA

Otter.ai

Senior Machine Learning Engineer

Mountain View, CA · On-site +1

$230K - $265K/yr

As a Senior Machine Learning Engineer, you'll bring your strong software engineering mindset to machine learning in order to scale and optimize our ML systems-creating and transforming innovative ...

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All JobsMachine Learning Engineer JobsRemote Machine Learning Engineer JobsRemote Machine Learning Engineer Jobs in Livermore, CA

Remote Machine Learning Engineer information

See Livermore, CA salary details

$36.9K

$151K

$227K

How much do remote machine learning engineer jobs pay per year?

As of Aug 18, 2026, the average yearly pay for remote machine learning engineer in Livermore, CA is $151,044.00, according to ZipRecruiter salary data. Most workers in this role earn between $119,100.00 and $181,800.00 per year, depending on experience, location, and employer.

What is a remote machine learning engineer?

A Remote Machine Learning Engineer designs, develops, and deploys machine learning models while working from a remote location. They preprocess data, train and optimize models, and integrate them into production systems. Their role often involves collaborating with data scientists, software engineers, and stakeholders to solve complex problems using AI. Strong programming skills in Python, experience with ML frameworks like TensorFlow or PyTorch, and cloud computing knowledge are essential. Remote ML engineers must also communicate effectively and manage their time efficiently to work asynchronously with teams.

What are the key skills and qualifications needed to thrive as a remote machine learning engineer?

To thrive as a Remote Machine Learning Engineer, you need a strong background in computer science, mathematics, and experience with machine learning algorithms, typically supported by a relevant degree and prior project work. Proficiency with programming languages like Python, machine learning frameworks such as TensorFlow or PyTorch, and familiarity with cloud computing platforms is crucial, and certifications like AWS Certified Machine Learning can enhance your profile. Excellent communication, self-motivation, and time-management skills are also essential for collaborating across remote teams and meeting project goals. These combined technical and soft skills are vital for developing effective machine learning solutions while ensuring productivity and collaboration in a virtual work environment.

What are some typical challenges faced by remote machine learning engineers, and how are they addressed?

Remote Machine Learning Engineers often face challenges such as coordinating across different time zones, ensuring smooth communication with team members, and accessing large datasets or secure environments remotely. Organizations commonly address these by using robust collaboration tools (like Slack, GitHub, and Jira), establishing clear documentation, and setting regular virtual meetings to maintain alignment. Many companies also provide secure remote environments or VPN access for handling sensitive data and code. Proactive communication and organized workflows help mitigate these challenges, enabling engineers to remain productive and connected to their teams.

Are remote machine learning engineers still in demand?

Remote machine learning engineers are currently in high demand due to the growth of AI and data-driven technologies across industries. Skills in programming, data analysis, and familiarity with tools like Python, TensorFlow, or PyTorch are highly sought after, and many companies continue to hire for remote roles in this field.

Can remote machine learning engineers work remotely?

Yes, remote machine learning engineers can work remotely, as many companies offer flexible work arrangements for this role. The position typically involves tasks such as data analysis, model development, and collaboration through online tools, making remote work feasible with strong communication skills and proficiency in programming languages like Python or frameworks like TensorFlow. However, some roles may require occasional on-site meetings or access to specialized hardware.

What are the most commonly searched types of Machine Learning Engineer jobs in Livermore, CA?

The most popular types of Machine Learning Engineer jobs in Livermore, CA are:

What are popular job titles related to Remote Machine Learning Engineer jobs in Livermore, CA?

For Remote Machine Learning Engineer jobs in Livermore, CA, the most frequently searched job titles are:

What job categories do people searching Remote Machine Learning Engineer jobs in Livermore, CA look for?

The top searched job categories for Remote Machine Learning Engineer jobs in Livermore, CA are:

What cities near Livermore, CA are hiring for Remote Machine Learning Engineer jobs?

Cities near Livermore, CA with the most Remote Machine Learning Engineer job openings:

Remote Machine Learning Engineer jobs near you
Infographic showing various Remote Machine Learning Engineer job openings in Livermore, CA as of August 2026, with employment types broken down into 1% As Needed, 73% Full Time, 22% Part Time, 1% Temporary, and 3% Contract. Highlights an 89% Physical, 2% Hybrid, and 9% Remote job distribution, with an average salary of $151,044 per year, or $72.6 per hour.

Lead Machine Learning Engineer - Remote (US) or CA - Only W2

Saransh Inc

Mountain View, CA • Remote

$104K - $138K/yr

Contractor

Re-posted 2 days ago

Job description

Role: Lead Machine Learning Engineer
Location: Mountain View, CA (3 days a week onsite) (OR) Remote
Job Type: W2 Contract
Duration: 12 months
 
 
Experience: Senior/Lead Level
 
Short Overview of JD:
Looking for a ML Engineer who will be working on the products related to seismic and well log data, identifying simple geologic characteristics of the data (faults, horizons), and working knowledge of the different subsurface data formats and types.
 
Primary Skills:
MLOps, Deep learning, GPU training and inference, Image models, GCP, TensorFlow, PyTorch, Agentic coding tools
 
We are looking for a candidate who:
  • Senior-level experience leading small engineering teams, setting technical goals in a business context, and remaining hands-on.
  • Familiarity with agentic coding tools (e.g., Claude).
  • Is well-versed in deep learning, GPU training and inference, and image models.
  • Has extensive experience in model training and setting up distributed model training pipelines, especially using platforms like Vertex AI and Kubeflow for large-scale image and language model training.
  • Possesses a strong background in building and deploying machine learning models, with a focus on image processing and time series signal processing.
  • Has hands-on experience in training and fine-tuning ML models.
  • Is skilled in building and maintaining data pipelines for image and sensor data.
  • Is familiar with ML Ops tools and practices, including model monitoring, versioning, and deployment.
  • Has experience working with data labeling tools.
  • Is comfortable with cloud platforms, particularly Google Cloud Platform (GCP); experience with edge deployments is a plus.
Additional (Nice to Have) Skills:
  • Experience with GCP is highly desirable; if not, the ability and willingness to learn quickly is expected.
  • Proficiency in TensorFlow and PyTorch.
  • Familiarity with Protocol Buffers and containerization technologies.
  • Experience with rapid prototyping to validate hypotheses.