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Remote Machine Learning Jobs in Stanford, CA (NOW HIRING)

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Remote Machine Learning information

See Stanford, CA salary details

$30K

$50K

$103.4K

How much do remote machine learning jobs pay per year?

As of Aug 9, 2026, the average yearly pay for remote machine learning in Stanford, CA is $50,034.00, according to ZipRecruiter salary data. Most workers in this role earn between $38,200.00 and $54,000.00 per year, depending on experience, location, and employer.

What is a remote machine learning job?

A remote machine learning job involves working with algorithms, data, and models to develop predictive systems or automate tasks, all while working from a location outside of a traditional office setting. Professionals in this role use techniques from statistics and computer science to analyze data, train machine learning models, and deploy solutions for real-world applications. Remote machine learning jobs can span various industries, including technology, healthcare, finance, and e-commerce. These roles typically require strong programming skills, knowledge of machine learning frameworks, and the ability to communicate findings effectively with team members or stakeholders. Working remotely offers flexibility, but also requires discipline and self-motivation to succeed.

What are some effective strategies for collaborating with team members while working remotely as a machine learning engineer?

Collaboration in a remote Machine Learning role often relies on clear communication through digital tools such as Slack, Zoom, and project management platforms like Jira or Asana. Regular check-ins and stand-up meetings help keep everyone aligned on project goals and timelines. Sharing code and models via version control systems (like Git) and using collaborative notebooks (such as JupyterHub or Google Colab) are also common practices. Building strong documentation habits and proactively seeking feedback can help ensure smooth teamwork and project success, even across different time zones.

What is the difference between Remote Machine Learning vs Data Scientist?

AspectRemote Machine LearningData Scientist
Required CredentialsBachelor's/Master's in CS, ML certificationsBachelor's/Master's in CS, Statistics, or related field
Work EnvironmentRemote, collaborative teams, tech companiesRemote or on-site, diverse industries, analytics focus
Industry UsageTech, AI startups, researchFinance, healthcare, e-commerce, tech
Search & Comparison IntentOften compared for technical roles in AI/MLBroader data analysis roles, but overlapping skills

Remote Machine Learning specialists focus on developing algorithms and models primarily in tech environments, often requiring advanced programming and ML knowledge. Data Scientists analyze data to extract insights, sometimes utilizing ML techniques. While both roles share skills and credentials, Remote Machine Learning emphasizes model development, whereas Data Scientists focus on data analysis and interpretation.

Can remote machine learning engineers work remotely?

Yes, remote machine learning engineers can work remotely, as many companies offer flexible work arrangements for data science and AI roles. These positions typically require strong programming skills, experience with tools like Python and TensorFlow, and the ability to collaborate virtually using communication platforms. Remote work in this field is common, especially for roles focused on model development, data analysis, and deployment.
More about Remote Machine Learning jobs
What are the most commonly searched types of Machine Learning jobs in Stanford, CA? The most popular types of Machine Learning jobs in Stanford, CA are:
What job categories do people searching Remote Machine Learning jobs in Stanford, CA look for? The top searched job categories for Remote Machine Learning jobs in Stanford, CA are:
What cities near Stanford, CA are hiring for Remote Machine Learning jobs? Cities near Stanford, CA with the most Remote Machine Learning job openings:
Remote Machine Learning jobs near you
Infographic showing various Remote Machine Learning job openings in Stanford, CA as of August 2026, with employment types broken down into 71% Full Time, and 29% Contract. Highlights an 100% Remote job distribution, with an average salary of $50,034 per year, or $24.1 per hour.

Principal Machine Learning Engineer, Artificial Intelligence (AI) Required, Work From Home

Ginas Tech Jobs

San Francisco, CA • Remote

Full-time

Medical, Dental, Vision, PTO

Re-posted 10 hours ago

Job description

Job Description

Principal Machine Learning Engineer, Artificial Intelligence (AI) Required, Work From Home

As a Principal Machine Learning Engineer, you are a deep technical authority responsible for designing and evolving the most critical ML systems in the company.  The Principal Machine Learning Engineer will operate across training, inference, evaluation, and infrastructure, solving the hardest architectural and performance problems.  While Technical Leads may own execution at the team level, you set the technical standard and shape how ML systems are built across the organization.  This is a hands-on, high-impact role focused on depth.  This position is 100% Remote.

Principal Machine Learning Engineer Responsibilities:

- Architect and build large-scale ML systems spanning data, training, evaluation, inference, and deployment.

- Design reproducible, high-performance training pipelines across GPU infrastructure.

- Architect inference systems that balance latency, throughput, cost, and reliability at scale.

- Design and maintain data systems for high-quality synthetic and real-world training data.

- Implement evaluation pipelines covering performance, robustness, safety, and bias, in partnership with research leadership.

- Own production deployment, including GPU optimization, memory efficiency, latency reduction, and scaling policies.

- Collaborate closely with application engineering to integrate ML systems cleanly into backend, mobile, and desktop products.

- Make pragmatic trade-offs and ship improvements quickly, learning from real usage.

- Work under real production constraints: latency, cost, reliability, and safety

Principal Machine Learning Engineer Outcomes:

- ML systems (training, inference, evaluation) are reliable, scalable, and meet defined performance targets.

- Models deployed to production achieve measurable quality improvements and meet user-impact goals.

- Production issues are proactively monitored, debugged, and resolved with clear root-cause analysis.

- Team and cross-functional collaborators benefit from clear guidance, best practices, and scalable ML solutions.

- Research-to-production cycles are efficient, safe, and continuously improve the product experience.

Qualifications

Principal Machine Learning Engineer Qualifications:

- Strong background in deep learning and transformer-based architectures.

- Artificial Intelligence (AI) experience required.

- Hands-on experience training, fine-tuning, or deploying large-scale ML models in production.

- Proficiency with at least one modern ML framework (e.g. PyTorch, JAX), and ability to learn others quickly.

- Experience with distributed training and inference frameworks (e.g. DeepSpeed, FSDP, Megatron, ZeRO, Ray).

- Strong software engineering fundamentals; you write robust, maintainable, production-grade systems.

- Experience with GPU optimization, including memory efficiency, quantization, and mixed precision.

- Comfort owning ambiguous, zero-to-one ML systems end-to-end.

- A bias toward shipping, learning fast, and improving systems through iteration.

- Experience with LLM inference frameworks such as vLLM, TensorRT-LLM, or FasterTransformer.

- Contributions to open-source ML or systems libraries.

- Background in scientific computing, compilers, or GPU kernels.

- Experience with RLHF pipelines (PPO, DPO, ORPO).

- Experience training or deploying multimodal or diffusion models.

- Experience with large-scale data processing (Apache Arrow, Spark, Ray).

Benefits include medical insurance, Dental, Vision, Savings Plan Options, PTO, etc.

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Additional Information

Please check out all of our jobs at www.ginastechjobs.com.