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AI Engineer - San Francisco

Distyl AI develops AI native technologies for humans & AI to collaborate to power the operations of the Global Fortune 1000.

In just 24 months, we’ve rapidly grown to partner with some of the world’s largest enterprises—including F100 telecom, healthcare, manufacturing, insurance, and retail companies—delivering multiple AI deployments with $100M+ impact. Our platform, Distillery, along with our team of AI Engineers, Researchers, and Strategists, is pioneering AI-native systems of work, solving the most complex, high-stakes challenges at scale.

Distyl is founded and led by proven leaders from companies like Palantir, Apple, and top national laboratories. We work in deep partnership with OpenAI, jointly going-to-market at the largest enterprises and collaborating evaluating and testing the latest models. Backed by Lightspeed, Khosla, Coatue, industry leaders like Nat Friedman (former GitHub CEO), as well as board members of over 20+ F500s, Distyl is building the future of AI-powered enterprise operations.

What We Are Looking For

As an AI Engineer at Distyl, you’ll be responsible for designing, implementing, and deploying AI-powered systems into production for some of the most iconic institutions in the world. You’ll partner closely with customers to understand their most critical challenges and deliver innovative, reliable solutions that harness the power of Large Language Models (LLMs). You’ll also contribute to the evolution of our internal platform, Distillery, shaping how enterprise-grade LLM applications are built and deployed.

You’ll be expected to bring technical depth, a builder’s mindset, and an obsession with quality and performance. You will work at the cutting edge of LLMs, evaluation models, designing responsible AI solutions, and implementing them into customer environments with speed and precision. You’ll also help evolve Distyl’s engineering best practices, bringing the discipline needed to scale.

This role requires strong software engineering skills, deep familiarity with machine learning workflows, and a bias toward practical, high impact solutions.

Key Responsibilities

  • LLM Systems Engineering: Design and implement robust LLM-powered systems that deliver real business value. This includes model evaluation, prompt design, agent logic, pilot development, and production deployment.

  • Customer Collaboration: Work closely with customer stakeholders to understand business pain points and technical constraints, then translate these into technical architectures and applications.

  • Platform Development: Contribute to the development of Distillery, our internal platform for LLM application development, by building reusable components, tools, and infrastructure to support the broader engineering team.

  • Production Quality: Ensure that deployed systems are observable, maintainable, and meet rigorous performance and reliability standards.

  • AI Evaluation: Apply a scientific and thoughtful approach to model vetting and evaluation, accounting for accuracy, explainability, cost, latency, and robustness.

  • Continuous Improvement: Drive improvements in system architecture, development workflows, and deployment processes across teams.

  • Tech Leadership: Mentor peers and contribute to a culture of technical excellence, collaboration, and curiosity.

What We Require

  • 3+ years of experience as a software or machine learning engineer.

  • Strong programming skills and experience with production software development.

  • Expertise in machine learning frameworks (e.g., PyTorch, Transformers, Scikit-learn), data libraries (e.g., NumPy, Pandas), and ML operations.

  • Strong understanding of AI/ML evaluation and how to operationalize responsible, reliable systems.

  • Experience with cloud infrastructure and modern deployment practices (e.g., CI/CD, Git, Docker, AWS/GCP/Azure).

  • Bachelor’s degree in Computer Science, Mathematics, or a related technical field.

  • Strong communication and collaboration skills, with the ability to work cross-functionally with customers and internal teams.

  • Passion for AI and a pragmatic approach to solving real-world problems.

What We Offer

  • Competitive salary and benefits package, including equity options, medical/dental/vision covered at 100% for you and your dependents, 401K plan, and perks such as commuter benefits and lunch provided in office.

  • Opportunity to be part of a mission-oriented company to create practical adoption during the biggest revolution in human productivity.

  • Opportunity to work on cutting-edge AI technologies and make a real impact on the adoption of LLMs in the enterprise space.

  • Collaborative and supportive work environment, with a strong focus on personal and professional growth.

  • Exposure to a diverse range of real-world industries and projects, providing unique challenges and learning opportunities.

Note: Distyl is a hybrid working environment and requires in-office collaboration 3 days a week. We have offices in San Francisco, CA and New York, NY, and this role is available in either location.

Average salary estimate

$135000 / YEARLY (est.)
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$120000K
$150000K

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What You Should Know About AI Engineer - San Francisco, Distyl AI

At Distyl AI, we are on the cutting edge of artificial intelligence, blending human capabilities with AI technology to revolutionize enterprise operations. As an AI Engineer based in San Francisco, you will play a pivotal role in harnessing the power of Large Language Models (LLMs) to deliver innovative solutions to some of the world’s most iconic institutions. Your expertise will help us design, implement, and deploy AI-powered systems that create real business impact. You'll collaborate with customers to deeply understand their needs and translate those into effective technical architectures. Your contributions to our internal platform, Distillery, will drive the evolution of how enterprise-grade LLM applications are built and deployed. We are looking for someone with a builder's mindset who thrives in fast-paced environments and is passionate about quality, performance, and responsible AI solutions. Joining Distyl means you’ll work alongside industry leaders from companies such as Palantir and Apple, and you’ll have the opportunity to influence engineering best practices and mentor other engineers. This position not only offers you a chance to work with cutting-edge technologies but also allows you to shape the future of AI in enterprise collaborations while enjoying a supportive and collaborative work environment in San Francisco, where we focus on personal and professional growth.

Frequently Asked Questions (FAQs) for AI Engineer - San Francisco Role at Distyl AI
What are the main responsibilities of an AI Engineer at Distyl AI?

As an AI Engineer at Distyl AI, you will be responsible for designing and implementing robust AI-powered systems, particularly focusing on Large Language Models (LLMs). This involves collaborating with customers to understand their business challenges, developing technical solutions, and contributing to the development of our internal platform, Distillery. You'll also ensure that the deployed systems meet high-quality standards while driving continuous improvements across the engineering team.

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What qualifications are required to apply for the AI Engineer position at Distyl AI?

To apply for the AI Engineer position at Distyl AI, candidates should have a Bachelor’s degree in Computer Science, Mathematics, or a related field, along with at least 3 years of experience in software or machine learning engineering. Strong programming skills, experience with machine learning frameworks like PyTorch or Scikit-learn, and a solid understanding of AI/ML evaluation and deployment practices are essential.

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What technologies will an AI Engineer work with at Distyl AI?

An AI Engineer at Distyl AI will work with various technologies including machine learning frameworks like PyTorch and Transformers, data manipulation libraries such as NumPy and Pandas, and cloud infrastructure services like AWS, GCP, or Azure. Familiarity with production software development tools such as Git and Docker is also important, as well as continuous integration and deployment (CI/CD) practices.

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How does Distyl AI support personal and professional growth for its AI Engineers?

At Distyl AI, we prioritize personal and professional growth by fostering a collaborative and supportive work environment. As an AI Engineer, you will have opportunities for mentorship, exposure to diverse projects across various industries, and opportunities to work with cutting-edge technologies. We also encourage continuous learning and improvement, allowing you to enhance your skills while making a significant impact in the field of AI.

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What is the work environment like for an AI Engineer at Distyl AI?

The work environment at Distyl AI is hybrid, requiring in-office collaboration three days a week. Our offices in San Francisco and New York foster teamwork and communication among our AI Engineers and other teams. We promote a balance of professional collaboration and individual work, ensuring an engaging atmosphere that enhances creativity and innovation.

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Common Interview Questions for AI Engineer - San Francisco
Can you explain your experience with Large Language Models?

When responding to this question, focus on specific projects where you've implemented LLMs, detailing the technologies you used, challenges you faced, and the outcomes. Highlight your understanding of model evaluation and deployment processes, showing how your contributions led to tangible business value.

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How do you approach collaboration with customers to understand their technical needs?

Discuss the strategies you use to engage with customers, such as conducting interviews or workshops to gather requirements, and how you translate their challenges into technical solutions. Emphasize the importance of communication and listening skills in building strong relationships.

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What machine learning frameworks are you most comfortable with, and why?

Mention the frameworks you have extensive experience with, such as PyTorch or TensorFlow, and share specific examples of projects where you utilized them. Discuss the unique features of these frameworks that make them suitable for your work in AI engineering.

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Describe a time you had to troubleshoot a complex AI system issue.

Provide a detailed example of a specific challenge you faced, the steps you took to diagnose the problem, and the final solution you implemented. Highlight your analytical skills and your proactive approach to issues in AI system performance.

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What does responsible AI mean to you, and how do you implement it?

Define responsible AI and explain its importance in your work. Discuss practices you follow to ensure ethical and transparent AI solutions, including model explanation and bias mitigation, and provide examples from your past experiences.

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Can you explain a project where you utilized cloud infrastructure for machine learning?

Discuss a specific project where you utilized cloud services such as AWS or GCP for deploying machine learning solutions. Explain how cloud infrastructure supported scalability, flexibility, and efficiency in that project, focusing on the technologies and practices you used.

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How do you stay current with developments in AI and machine learning?

Share the resources you utilize to keep up-to-date, such as research papers, online courses, webinars, and conferences. Discuss any communities you engage with, along with how you apply new knowledge to your work as an AI Engineer.

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What is your experience with software development practices like CI/CD?

Explain your familiarity with CI/CD practices, discussing specific tools and workflows you have implemented in your previous roles. Highlight examples of how these practices improved the efficiency and reliability of your deployments.

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How would you ensure the quality of an AI-powered system before deployment?

Discuss the methodologies you follow to identify and mitigate risks in AI systems before deployment, including testing processes, performance metrics, and user feedback. Highlight the importance of observability and maintainability in your evaluation approach.

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What soft skills do you think are essential for an AI Engineer, and why?

Discuss the importance of soft skills like communication, collaboration, and adaptability in the role of an AI Engineer. Provide examples of how these skills have helped you work effectively within teams and with clients to achieve successful outcomes.

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Distyl AI works with organisations to increase their AI readiness and develop integrated AI-based solutions that are specific to their unique data, workflows, and workforce requirements.

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Full-time, hybrid
DATE POSTED
March 28, 2025

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