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Remote Rf Engineer Jobs in Sunnyvale, CA (NOW HIRING)

David Joseph & Company

AI Engineer

San Francisco, CA · Remote

$150K - $250K/yr

AI Engineer Type: Full-time | Remote (US) -- San Francisco preferred Compensation: $150K - $250K + equity Visa sponsorship: Not available (TN available; no H-1B) About the Company A Series B health ...

Grantek

Systems Engineer

San Jose, CA · Remote

Coordinating work with remote teams across multiple time zones. * Mentoring colleagues and contributing expertise to the broader engineering team. * Managing task prioritization across 5-10 active ...

Okta

Demo Engineer

San Francisco, CA · Remote

Demo Engineer Demo Engineering sits at the intersection of go-to-market and product, serving as a ... Experience with Okta or Auth0 #LI-Remote #LI-CM P21866_3462403

Campfire

AI Engineer

San Francisco, CA · On-site +1

We are not open to remote candidates. About Us Campfire is on a mission to redefine the accounting ... The Role Join our team as an AI Engineer at Campfire, where you'll design and implement AI‑driven ...

Chima

AI Engineer

San Francisco, CA · On-site +1

$100K - $300K/yr

Join to apply for the AI Engineer role at Chima 1 year ago Be among the first 25 applicants Join to ... Remote) San Francisco, CA $167,000.00-$185,500.00 6 days ago San Francisco, CA $130,000.00-$160,000 ...

Oklo

Regulatory Engineer

Santa Clara, CA · On-site +1

From an engineering perspective, the Regulatory Engineer is expected to contribute to the design ... Oklo requires remote employees to travel to headquarters (Santa Clara, CA) twice a quarter annually ...

Oklo

Regulatory Engineer

Santa Clara, CA · On-site +1

Position Description The Regulatory Engineer is responsible for both navigating the regulatory ... Oklo requires remote employees to travel to headquarters (Santa Clara, CA) twice a quarter annually ...

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Remote Rf Engineer information

See Sunnyvale, CA salary details

$43.4K

$138.1K

$214.8K

How much do remote rf engineer jobs pay per year?

As of Jul 14, 2026, the average yearly pay for remote rf engineer in Sunnyvale, CA is $138,116.00, according to ZipRecruiter salary data. Most workers in this role earn between $114,400.00 and $163,100.00 per year, depending on experience, location, and employer.

What is a Remote RF Engineer job?

A Remote RF Engineer is a professional who designs, analyzes, and optimizes radio frequency (RF) systems while working remotely. They focus on tasks such as network planning, signal analysis, interference mitigation, and equipment testing for industries like telecommunications, aerospace, and defense. Using specialized software and tools, they ensure effective wireless communication without being physically present at a work site. This role requires knowledge of RF principles, antenna design, and wireless standards. Strong problem-solving skills and experience with RF simulation tools are essential for success in this position.

What are the key skills and qualifications needed to thrive in the Remote Rf Engineer position, and why are they important?

To thrive as a Remote RF Engineer, you need a strong background in radio frequency theory, wireless communication, circuit design, and a relevant engineering degree. Familiarity with RF simulation tools (such as CST, HFSS, or ADS), spectrum analyzers, and certifications like a Professional Engineer (PE) license or relevant vendor certifications are highly valued. Excellent problem-solving, self-management, and clear written and verbal communication skills distinguish top candidates. These skills are crucial as RF Engineers must independently analyze, design, and troubleshoot complex wireless systems while effectively collaborating with distributed teams.

What are the typical daily responsibilities of a Remote RF Engineer?

As a Remote RF Engineer, your daily responsibilities often include designing, simulating, and testing RF circuits and systems, diagnosing performance issues, and optimizing wireless networks from a remote location. You may collaborate virtually with cross-functional teams, prepare technical reports, and participate in project meetings. Many remote RF Engineers also support field teams by analyzing remote test data and providing guidance on troubleshooting. The role requires strong self-discipline and proactive communication to ensure timely project delivery and effective teamwork.

What are the most commonly searched types of Rf Engineer jobs in Sunnyvale, CA? The most popular types of Rf Engineer jobs in Sunnyvale, CA are:
What are popular job titles related to Remote Rf Engineer jobs in Sunnyvale, CA? For Remote Rf Engineer jobs in Sunnyvale, CA, the most frequently searched job titles are:
What job categories do people searching Remote Rf Engineer jobs in Sunnyvale, CA look for? The top searched job categories for Remote Rf Engineer jobs in Sunnyvale, CA are:
What cities near Sunnyvale, CA are hiring for Remote Rf Engineer jobs? Cities near Sunnyvale, CA with the most Remote Rf Engineer job openings:
Remote Rf Engineer jobs near you
GenAI Engineer - Remote

GenAI Engineer - Remote

MM International

San Francisco, CA • Remote

Contractor

Posted 25 days ago

Job description

GenAI/LLM Engineer (NLP, TensorFlow, PyTorch SME)

San Francisco, Bay Area, CA

Duration: Six months may extend to 12 months

Must be in the Greater Bay area – or in California

Domain: utilities

GenAI/LLM Engineer (NLP, TensorFlow, PyTorch SME)

Implementing GenAI requires specialized expertise in large language models. Traditional data scientists often haven't had the opportunity to dive deep into the practical intricacies of LLMs—particularly advanced fine-tuning techniques, model compression strategies, memory optimization approaches, and specialized training workflows. This role requires a hands-on deep learning practitioner comfortable with modern frameworks and libraries specific to LLM development.

  • Enables domain-specific fine-tuning of models to client's unique utility context
  • Improves model performance while reducing computational costs through advanced optimization techniques
  • Creates Client-specific AI capabilities that address our unique operational challenges
  • Enables the CoE to move beyond generic AI tools to customized solutions that deliver higher business value

Key Responsibilities:

  • Implement and optimize advanced fine-tuning approaches (LoRA, PEFT, QLoRA) to adapt foundation models to client's domain
  • Develop systematic prompt engineering methodologies specific to utility operations, regulatory compliance, and technical documentation
  • Create reusable prompt templates and libraries to standardize interactions across multiple LLM applications and use cases
  • Implement prompt testing frameworks to quantitatively evaluate and iteratively improve prompt effectiveness
  • Establish prompt versioning systems and governance to maintain consistency and quality across applications
  • Apply model customization techniques like knowledge distillation, quantization, and pruning to reduce memory footprint and inference costs
  • Tackle memory constraints using techniques such as sharded data parallelism, GPU offloading, or CPU+GPU hybrid approaches
  • Build robust retrieval-augmented generation (RAG) pipelines with vector databases, embedding pipelines, and optimized chunking strategies
  • Design advanced prompting strategies including chain-of-thought reasoning, conversation orchestration, and agent-based approaches
  • Collaborate with the MLOps engineer to ensure models are efficiently deployed, monitored, and retrained as needed

Expected Skillset:

  • Deep Learning & NLP: Proficiency with PyTorch/TensorFlow, Hugging Face Transformers, DSPy, and advanced LLM training techniques
  • GPU/Hardware Knowledge: Experience with multi-GPU training, memory optimization, and parallelization strategies
  • LLMOps: Familiarity with workflows for maintaining LLM-based applications in production and monitoring model performance
  • Technical Adaptability: Ability to interpret research papers and implement emerging techniques (without necessarily requiring PhD-level mathematics)
  • Domain Adaptation: Skills in creating data pipelines for fine-tuning models with utility-specific content