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AI Engineer

Xoul is reshaping the interaction between humans and AI. We're building an intuitive, powerful platform where anyone can create, deploy, and monetize intelligent AI agents and swarms that autonomously collaborate and execute meaningful actions in the real world. Our vision is bold: empowering creators to design intelligent, autonomous systems that amplify human potential.

What You'll Do

  • Solve Cutting-edge AI Problems: Relentlessly tackle novel AI/ML challenges—fine-tuning state-of-the-art models, creating innovative algorithms, and pushing boundaries. If you’re intimidated by ambiguity or give up easily, this isn’t your place.

  • Own Your Systems, Deploy Rapidly: You’ll handle AI/ML pipelines from data ingestion and preprocessing to inference optimization and deployment strategies. Move fast without sacrificing code or model quality.

  • Radical Accountability: Your responsibility doesn’t end at deployment. Debug, optimize, and proactively ensure your AI models and data pipelines perform robustly at scale in production.

  • Collaborative Specification: Actively flesh out project specs alongside executives, teammates, and product engineers. You're a partner, not an executor.

  • Deep User Understanding: You're not just deploying models; you're creating intelligent systems that deeply resonate with users. Anticipate and accommodate user needs proactively in your AI systems.

  • Embrace Transparency: Clearly communicate progress, challenges, and blockers. Transparency and proactive communication are critical.

  • Work Exceptionally Hard: This isn't FAANG. If work-life balance is your main priority, this isn’t your job. We work relentlessly and expect you to keep pace—because we're building something groundbreaking.

Your AI Stack & Skillset

You should be comfortable—or rapidly become comfortable—with:

  • Classical ML Algorithms: Deep familiarity with supervised and unsupervised learning techniques, including traditional and deep-learning methods.

  • Data Pipelines & Infrastructure: Experience with pipeline tools like Kafka, Dagster/Airflow, Flink, or equivalent streaming and batch processing systems.

  • Model Fine-tuning & Pre-training: Hands-on experience with fine-tuning and customizing state-of-the-art models (LLMs, transformers, diffusion models, etc.) for production scenarios.

  • Inference Optimization & Low-Level Engines: Proficiency with performance-optimized inference engines such as TensorRT, FlashInfer, and direct experience programming CUDA kernels.

  • High-level Model Deployment Tools: Comfortable deploying models using frameworks like vLLM, Triton, or similar deployment environments.

  • Production-Grade AI Systems: Experience deploying, scaling, and maintaining AI/ML systems in production environments serving real-world users.

  • Research & Innovation: Prior research experience is encouraged. You will regularly confront novel, ambiguous problems requiring innovative solutions.

  • Systems Experience (Bonus): A background in traditional software development and system design is a plus.

Being an LLM whisperer—someone exceptionally skilled at coaxing maximum performance out of LLMs—is a significant bonus.

About You

  • Proven AI Builder: 2+ years experience building, deploying, and scaling impactful AI/ML systems that real users rely on daily.

  • Deep Product Intuition: You don’t just deploy models; you anticipate user needs, proactively crafting intelligent, user-aligned solutions.

  • Startup DNA: You thrive amid ambiguity, adapt quickly, move swiftly, and genuinely love the intensity of startup life.

  • Ownership Mentality: "Not my problem" isn’t in your vocabulary. If you build it, you own it completely.

  • Relentless Problem Solver: You get excited—not discouraged—when facing challenging, ambiguous AI problems.

  • Passionate about AI: Deep curiosity and passion for AI, its emerging techniques, and practical applications.

Why Xoul?

At Xoul, we're pioneering a shift from isolated AI interactions to powerful, autonomous agent swarms capable of seamless collaboration and meaningful action. You'll join a passionate, driven team of product obsessives and exceptional engineers, committed to crafting transformative AI experiences.

We’re ambitious, perfectionistic, and fiercely passionate about the AI future we're shaping. If you're an AI expert craving high-impact work, Xoul is where the next AI revolution will unfold.

🛏️ Housing is on us: We cover your housing costs if you come live and work with us, so you can fully focus on redefining the AI landscape.

TL;DR: If you're an ambitious AI builder, comfortable navigating cutting-edge challenges and deploying sophisticated ML systems, and excited to shape the next wave of autonomous AI experiences, apply now.

Average salary estimate

$150000 / YEARLY (est.)
min
max
$120000K
$180000K

If an employer mentions a salary or salary range on their job, we display it as an "Employer Estimate". If a job has no salary data, Rise displays an estimate if available.

What You Should Know About AI Engineer, Xoul Inc

Meet Xoul, where we're transforming the relationship between humans and AI, creating a platform that enables anyone to design, deploy, and profit from intelligent AI agents and swarms. As an AI Engineer at Xoul in San Francisco, you'll tackle the most exciting AI and machine learning challenges, fine-tuning models and developing innovative algorithms that push the boundaries of what's possible. You won't just deploy solutions; you'll build and own a robust AI/ML pipeline, from data ingestion to deployment, ensuring high performance even at scale. Collaboration is at our core, meaning you'll work closely with executives and product engineers, helping shape the future of our products. We believe in understanding users deeply, and your role will be to anticipate their needs as you create intelligent systems. Transparency is key in our team, so you'll need to communicate progress, challenges, and results effectively. Be prepared to work hard; we're not your typical company, and our commitment to innovation means that a balance between life and work may look different here. If you have 2+ years of experience with deploying AI solutions that users depend on and have a relentless passion for problem-solving, Xoul is the place for you. Join us as we drive significant AI advancements and enjoy the perks, including covered housing costs to help you focus entirely on your groundbreaking work. If you're ready to embark on this exciting journey with us, we want to hear from you!

Frequently Asked Questions (FAQs) for AI Engineer Role at Xoul Inc
What are the main responsibilities of an AI Engineer at Xoul?

An AI Engineer at Xoul will be responsible for solving cutting-edge AI and ML problems, owning the AI/ML pipelines from ingestion to deployment, and ensuring the performance of systems in production. This role involves collaborative specification, a deep understanding of user needs, and embracing transparency throughout the project lifecycle.

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What qualifications do I need to apply for the AI Engineer position at Xoul?

To apply for the AI Engineer position at Xoul, candidates should have a minimum of 2 years of experience in building, deploying, and scaling impactful AI/ML systems. A strong understanding of classical ML algorithms, data pipelines, model fine-tuning, and experience with production-grade AI systems are essential qualifications.

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How does Xoul support innovative solutions in AI Engineering?

Xoul encourages AI Engineers to embrace innovation by confronting novel and ambiguous problems regularly. We support an environment where experimentation is valued, allowing engineers to develop creative solutions that push the boundaries of AI applications.

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What skills are important for an AI Engineer at Xoul?

Important skills for an AI Engineer at Xoul include proficiency in classical machine learning algorithms, experience with data pipelines and infrastructure, and familiarity with model fine-tuning and production deployment tools like TensorRT and Triton. A background in systems design and traditional software development is also advantageous.

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What can I expect from the company culture at Xoul?

The culture at Xoul is fast-paced and demanding, where employees are expected to work hard and contribute meaningfully to groundbreaking projects. We thrive on transparency, accountability, and collaboration, and our team shares a collective passion for reshaping the AI landscape.

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Common Interview Questions for AI Engineer
Can you describe your experience with deploying AI models in a production environment?

When answering this question, discuss the specific models you've deployed, the challenges faced during deployment, and how you ensured their performance and scalability. Share metrics or outcomes to illustrate your impact.

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How do you approach problem-solving in AI/ML projects?

Describe your methodical approach to tackling ambiguous problems, including how you research, brainstorm, prototype, and iterate solutions. Highlight any frameworks or tools you use to facilitate this process.

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What AI/ML algorithms are you most comfortable with, and how have you applied them?

Discuss your familiarity with both traditional and deep learning techniques. Provide examples of projects where you've applied these algorithms successfully, emphasizing the results achieved.

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How do you ensure the quality and robustness of an AI system after deployment?

Explain your process for monitoring AI system performance post-deployment, including techniques for debugging and optimization. Discuss any tools or metrics you utilize to measure success.

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How do you understand user needs when developing AI solutions?

Illustrate your methods for gathering user insights, such as user interviews, feedback loops, and usability testing. Emphasize your focus on aligning solutions with user requirements and enhancing user experience.

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Can you provide an example of a challenging AI problem you've solved?

Share a specific challenge you faced in an AI project, the steps you took to address it, and the impact of your solution. Highlight your problem-solving skills and creativity.

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What tools and technologies do you use for building AI models?

Talk about the specific frameworks, libraries, and tools you prefer for building AI models, such as TensorFlow, PyTorch, or Scikit-learn, and how they contribute to your workflow.

Join Rise to see the full answer
What is your experience with data pipelines for AI/ML applications?

Detail your experience in creating, managing, and optimizing data pipelines. Mention tools you've used, such as Apache Kafka or Airflow, and how you've ensured data quality and consistency.

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How do you stay current with emerging trends in AI?

Describe your proactive approach to learning and professional development. Highlight sources like research papers, online courses, and conferences that you use to stay informed.

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What do you think is the future of AI, and how can you contribute to it?

Share your insights on emerging trends in AI and discuss your vision for the future. Explain how your skills and experiences align with driving these advancements in the industry.

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Full-time, on-site
DATE POSTED
March 30, 2025

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