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Python Llm Jobs in Mountain View, CA (NOW HIRING)

Whatnot

LLM Platform Engineer

San Francisco, CA · On-site

$245K - $345K/yr

  • Medical

  • Dental

  • Vision

  • Retirement

Create robust and scalable LLM evaluation frameworks to measure model performance, guide iteration ... Python * Ability to work autonomously and drive initiatives across multiple product areas and ...

Sciforium

LLM Dataset Engineer

San Francisco, CA · On-site

$155K - $210K/yr

  • Medical

  • Dental

  • Vision

  • Retirement

Develop high-throughput data processing scripts using Python, leveraging multiprocessing and ... Experience building massive LLM training sets from scratch , including raw web crawls (e.g., Common ...

Fortinet

Staff Software Development Engineer (LLM)

Sunnyvale, CA · On-site

$196K - $219K/yr

  • Medical

  • Dental

  • Vision

  • Life

  • Retirement

  • PTO

Strong programming and debugging skills, particularly in Python and C/C++. * Familiarity with ... LLM APIs (OpenAI, etc.) and vector databases is beneficial. Must be authorized to work in the U.S ...

Apple

Sr Python Backend Engineer, AIML

Cupertino, CA · On-site

The AI and Machine Learning team is looking for Sr Python Backend Engineer to build world-class ... LLM-powered workflows, AI agents, and modern AI-assisted development practices. Minimum ...

NIO

LLM Algorithmic Optimization Engineer

San Jose, CA · On-site

$143K - $186K/yr

  • Medical

  • Dental

  • Vision

  • Life

  • Retirement

  • PTO

Proficiency in Python and experience with AI-related training and inference tools such as PyTorch. * Proficiency in C/C++ programming, familiar with at least one commonly used LLM inference engines.

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Python Llm information

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How much do python llm jobs pay per hour?

As of Aug 19, 2026, the average hourly pay for python llm in Mountain View, CA is $69.15, according to ZipRecruiter salary data. Most workers in this role earn between $57.02 and $78.56 per hour, depending on experience, location, and employer.

What is a Python LLM?

A Python LLM job involves working with Large Language Models (LLMs) using Python to develop, fine-tune, and deploy AI models. Responsibilities may include data preprocessing, prompt engineering, model optimization, and integration with applications. Professionals in this role often work with frameworks like TensorFlow, PyTorch, or Hugging Face Transformers. They may also contribute to improving model efficiency, reducing bias, and ensuring ethical AI usage.

What are the key skills and qualifications needed to thrive in the Python LLM position, and why are they important?

To excel as a Python LLM (Large Language Model) Engineer, you need strong skills in Python programming, machine learning, and natural language processing, typically supported by a degree in computer science or a related field. Proficiency with libraries such as TensorFlow, PyTorch, Hugging Face Transformers, and experience with model deployment platforms are often essential, alongside certifications in AI or data science. Effective communication, problem-solving abilities, and collaboration are important soft skills for working in interdisciplinary teams and delivering results in dynamic environments. These skills ensure the development, fine-tuning, and deployment of advanced language models that meet both technical and business objectives.

What are some common challenges faced by Python LLM engineers in their daily work?

Python LLM Engineers often encounter challenges related to optimizing model performance, managing large datasets, and adapting models to specific business needs. Working with large-scale language models requires balancing computational resource limitations with the need for high accuracy and efficiency. Collaboration with data scientists, product managers, and DevOps engineers is routine to ensure seamless model integration and deployment. Staying updated on the latest advancements in NLP and continuously improving models based on user feedback are also important aspects of the role.

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LLM Platform Engineer

Whatnot

San Francisco, CA • On-site

$245K - $345K/yr

Other

Medical, Dental, Vision, Retirement

Re-posted 10 days ago

Job description

Join the Future of Commerce with Whatnot!
Whatnot is the largest live shopping platform in North America and Europe to buy, sell, and discover the things you love. Whether it's trading cards, fashion, electronics, or live plants, our sellers are building real businesses across hundreds of categories. We're building live commerce at a scale that's never been done in the West, and there's no playbook to copy. The people here are shaping how an entirely new industry develops.
As a remote co-located team, we're inspired by our values and anchored in hubs across the US, UK, Ireland, Poland, Germany, and Australia. We move fast, stay close to our users, and focus on the work that drives the most impact.
We're one of the fastest growing marketplaces and were recently named the #1 Best Startup Employer in America by Forbes. Check out the latest Whatnot updates on our news and engineering blogs and join us as we enable anyone to turn their passion into a business and bring people together through commerce.
Role
We're looking for builders-intellectually curious, highly entrepreneurial engineers eager to shape the future of AI and ML at Whatnot. You'll design and scale the core infrastructure that powers large language model applications across the company, working side by side with machine learning scientists to bring cutting-edge models into production and unlock entirely new product experiences. This means building systems that make AI dependable and fast at scale-from building retrieval systems to more effectively ground LLM responses in Whatnot's business context to developing scalable LLM evaluation frameworks and human-in-the-loop feedback mechanisms.
What you'll do:
  • Own the infrastructure powering LLMs across critical business surfaces- supporting growth, recommendations, trust and safety, fraud, seller tooling, and more.
  • Create robust and scalable LLM evaluation frameworks to measure model performance, guide iteration, and prevent regression via CI/CD.
  • Deploy RAG systems and MCP servers to more effectively ground LLM responses in Whatnot's business context while enforcing rigorous PII controls.
  • Design efficient human-in-the-loop feedback pipelines that can be used to inform scalable LLM evaluation
  • Bridge the gap between research and production, helping to transform experimental ideas into scalable solutions
  • Stretch beyond your comfort zone to take on new technical challenges as we scale AI across Whatnot's ecosystem.

US Based: We offer flexibility to work from home or from one of our global office hubs, and we value in-person time for planning, problem-solving, and connection. Team members in this role must live within commuting distance of our New York, Seattle, Los Angeles, and San Francisco hubs.
You
Curious about who thrives at Whatnot? We've found that low ego, a growth mindset, and leaning into action and high impact goes a long way here.
As our next AI/ML Engineer you should have 4+ years of professional experience developing machine learning systems and algorithms, plus:
  • Bachelor's degree in Computer Science, Statistics, Applied Mathematics or a related technical field, or equivalent work experience.
  • 3+ years of software engineering experience building and maintaining production systems for consumer-scale loads.
  • 1+ years of professional experience developing software in Python
  • Ability to work autonomously and drive initiatives across multiple product areas and communicate findings with leadership and product teams.
  • Experience with operational, search, and key-value databases such as PostgreSQL, DynamoDB, Elasticsearch, Redis.
  • Firm grasp of visualization tools for monitoring and logging e.g. DataDog, Grafana.
  • Familiarity with cloud computing platforms and managed services such as AWS Sagemaker, Lambda, Kinesis, S3, EC2, EKS/ECS, Apache Kafka, Flink.
  • Professionalism around collaborating in a remote working environment and well tested, reproducible work.
  • Exceptional documentation and communication skills.
Compensation
For US-based applicants:$245,000 - $345,000/year + benefits + stock options
The salary range may be inclusive of several levels that would be applicable to the position. Final salary will be based on a number of factors including, level, relevant prior experience, skills and expertise. This range is only inclusive of base salary, not benefits (more details below) or equity in the form of stock options.
Benefits
  • Flexible Time off Policy and Company-wide Holidays (including a spring and winter break)
  • Health Insurance options including Medical, Dental, Vision
  • Work From Home Support
    • Home office setup allowance
    • Monthly allowance for cell phone and internet
  • Care benefits
    • Monthly allowance for wellness
    • Annual allowance towards Childcare
    • Lifetime benefit for family planning, such as adoption or fertility expenses
  • Retirement; 401k offering for Traditional and Roth accounts in the US (employer match up to 4% of base salary) and Pension plans internationally
  • Monthly allowance to dogfood the app
    • All Whatnauts are expected to develop a deep understanding of our product. We're passionate about building the best user experience, and all employees are expected to use Whatnot as both a buyer and a seller as part of their job (our dogfooding budget makes this fun and easy!).
  • Parental Leave
    • 16 weeks of paid parental leave + one month gradual return to work *company leave allowances run concurrently with country leave requirements which take precedence.
EOE
Whatnot is proud to be an Equal Opportunity Employer. We value diversity, and we do not discriminate on the basis of race, religion, color, national origin, gender, sexual orientation, age, marital status, veteran status, parental status, disability status, or any other status protected by local law. We believe that our work is better and our company culture is improved when we encourage, support, and respect the different skills and experiences represented within our workforce.

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