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Machine Learning Engineer - Fine Tuning

ABOUT BASETEN

Join our dynamic team at Baseten, where we’re revolutionizing AI deployment with cutting-edge inference infrastructure. Backed by premier investors such as IVPSpark CapitalGreylock, and Conviction, we’re trusted by leading enterprises and AI-driven innovators—including DescriptBland.aiPatreonWriter, and Robust Intelligence—to deliver top-tier performance, security, and reliability for their production workloads. With our recent $75 million Series C funding, we’re poised to accelerate our mission to make AI accessible across all products. If you’re passionate about tackling impactful challenges and building transformative solutions from the ground up, we invite you to join us on this exciting journey!

THE ROLE:

As a Machine Learning Engineer specializing in Fine-Tuning at Baseten, you'll create exceptional value for customers by leveraging our world-class infrastructure to fine-tune large language models and/or other modalities, working directly with customers to achieve their specific goals. You'll build scalable pipelines, implement parameter-efficient techniques, and ensure a seamless transition to inference. This customer-facing role requires both technical expertise in foundation model adaptation and the ability to translate customer needs into effective solutions. You'll also help shape our product roadmap by identifying common patterns in customer requirements and working with product teams to develop reusable components and features , reducing the need for custom services and streamlining the fine-tuning process for everyone.

RESPONSIBILITIES:

  • Design comprehensive fine-tuning strategies that translate customer requirements into effective technical approaches—finding the optimal combination of data preparation, training techniques, and evaluation methods to deliver solutions that precisely address customer needs

  • Develop tools to enable non-ML experts to fine-tune models effectively

  • Design and implement scalable fine-tuning pipelines for large language models and other AI modalities

  • Work directly with customers to understand requirements and guide technical implementation

  • Serve as the technical point of contact for customers throughout their fine-tuning journey

  • Utilize state-of-the-art parameter-efficient fine-tuning methods (LoRA, QLoRA)

  • Build systems for efficient data preparation, evaluation, and deployment of fine-tuned models

  • Research and apply cutting-edge techniques in instruction tuning and model customization

  • Create frameworks to evaluate fine-tuned model performance against base models

  • Implement best-in-class distributed training techniques like FSDP and DDP across various hardware configurations

REQUIREMENTS:

  • Bachelor’s degree in Computer Science, Engineering, or related field

  • 3+ years of experience in ML engineering with focus on model training and fine-tuning

  • Experience with advanced fine-tuning frameworks such as Axolotl, Unsloth, Transformers, TRL, PyTorch Lightning, or Torch Tune, enabling efficient model adaptation and optimization

  • Hands-on experience fine-tuning or pre-training LLMs or other foundation models

  • Excellent communication skills for explaining complex concepts to varied audiences

NICE TO HAVE:

  • Experience working with customers to deliver technical solutions

  • Track record of delivering ML projects to enterprise customers

  • Knowledge of distributed training systems and efficiency optimization techniques

  • Experience with advanced alignment and adaptation techniques including RLHF, DPO, constitutional AI, prompt tuning, reinforcement learning with execution feedback, PPO, or other emerging alignment methods

  • Knowledge of prompt engineering and domain adaptation methods

  • Contributions to open-source fine-tuning projects or tools

  • Experience building user-friendly interfaces for fine-tuning workflows

  • Experience with cloud platforms (AWS, GCP, Azure) and containerization technologies

BENEFITS:

  • Competitive compensation package (Flexible PTO, covered healthcare premiums for you and your dependents)

  • A unique opportunity to be part of a rapidly growing startup in one of the most exciting engineering fields of our era

  • An inclusive and supportive work culture that fosters learning and growth

  • Exposure to a variety of ML startups, offering unparalleled learning and networking opportunities

Apply Now to embark on a rewarding journey in shaping the future of AI! If you are a motivated individual with a passion for machine learning and a desire to be part of a collaborative and forward-thinking team, we would love to hear from you.

At Baseten, we are committed to fostering a diverse and inclusive workplace. We provide equal employment opportunities to all employees and applicants without regard to race, color, religion, gender, sexual orientation, gender identity or expression, national origin, age, genetic information, disability, or veteran status.

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What You Should Know About Machine Learning Engineer - Fine Tuning, Baseten

Join our dynamic team at Baseten as a Machine Learning Engineer specializing in Fine Tuning, and be part of a revolution in AI deployment! In this exciting role, you will leverage our world-class infrastructure to fine-tune large language models and actively work with our customers to meet their specialized goals. You'll be crafting scalable pipelines and implementing innovative techniques to seamlessly transition models to inference. With your technical know-how in adapting foundation models, you’ll play a crucial role in shaping our product roadmap while translating customer requirements into effective solutions. You’ll also have the chance to identify trends in customer needs and collaborate with product teams to create reusable components that streamline the fine-tuning process. This role at Baseten isn’t just about the technology, it's about connecting with customers and delivering transformative solutions. If you’re ready to tackle meaningful challenges and build impactful solutions from the ground up, we’d love for you to join us on this incredible journey. Together, let's make AI accessible and wonderful for everyone!

Frequently Asked Questions (FAQs) for Machine Learning Engineer - Fine Tuning Role at Baseten
What are the responsibilities of a Machine Learning Engineer - Fine Tuning at Baseten?

As a Machine Learning Engineer - Fine Tuning at Baseten, your responsibilities include designing tailored fine-tuning strategies that address customer requirements, developing tools for non-ML experts, creating scalable fine-tuning pipelines for various AI modalities, and guiding customers through their fine-tuning journey. You will also serve as the main technical point of contact and actively research and implement cutting-edge techniques in model customization.

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What qualifications are needed for the Machine Learning Engineer - Fine Tuning position at Baseten?

To be considered for the Machine Learning Engineer - Fine Tuning position at Baseten, candidates should have at least a Bachelor's degree in Computer Science or a related field, along with 3+ years of experience in ML engineering focusing on model training and fine-tuning. Familiarity with frameworks such as PyTorch, Transformers, and experience fine-tuning large language models are essential. Good communication skills are also crucial for explaining complex concepts to diverse audiences.

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What benefits can I expect when working as a Machine Learning Engineer - Fine Tuning at Baseten?

Working as a Machine Learning Engineer - Fine Tuning at Baseten comes with a competitive compensation package, including flexible PTO and healthcare premium coverage for you and your dependents. Additionally, you will have the unique opportunity to be part of a rapidly growing startup, gain exposure to various ML startups, and work within an inclusive work culture that fosters learning and personal growth.

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How does the Machine Learning Engineer - Fine Tuning role facilitate customer success at Baseten?

The Machine Learning Engineer - Fine Tuning role is pivotal in enabling customer success at Baseten by translating complex technical requirements into actionable strategies. By working closely with customers, you will ensure their needs are met effectively, providing state-of-the-art solutions that leverage our advanced infrastructure, and guiding them through the fine-tuning and deployment process, ultimately leading to enhanced performance and satisfaction.

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What tools and technologies should a Machine Learning Engineer - Fine Tuning at Baseten be familiar with?

A Machine Learning Engineer - Fine Tuning at Baseten should be well-versed in advanced fine-tuning frameworks such as Axolotl and PyTorch Lightning, as well as foundational models and distributed training systems. Familiarity with parameter-efficient fine-tuning methods like LoRA and QLoRA, along with cloud platforms like AWS, GCP, or Azure, is also beneficial for success in this role.

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Common Interview Questions for Machine Learning Engineer - Fine Tuning
Can you describe your experience with fine-tuning large language models?

In your response, highlight specific projects where you successfully fine-tuned language models. Mention the techniques and frameworks you employed, the results of your efforts, and how these experiences prepared you for the role at Baseten.

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What strategies do you use for designing fine-tuning workflows?

Explain your approach to understanding customer requirements, data preparation, choosing the right training techniques, and how you evaluate model performance. Include any specific frameworks or tools you have utilized in past projects.

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How do you ensure effective communication with non-technical stakeholders?

Discuss your previous experiences where you successfully communicated complex technical concepts to non-technical audiences. Emphasize the importance of tailoring your explanations to match their understanding and ensuring clear and open communication.

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What is your approach to developing scalable fine-tuning pipelines?

Share your experience in building and maintaining pipelines that can handle large datasets and multiple models. Mention any tools or programming languages you've used and how your methods have improved efficiency in model training.

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How do you stay updated on the latest advancements in machine learning and AI?

Talk about the resources you utilize, such as research papers, online courses, webinars, or professional communities. Emphasize your commitment to continuous learning and how this benefits your role as a Machine Learning Engineer - Fine Tuning.

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Describe a challenging problem you faced while fine-tuning a model and how you solved it.

Provide a specific example of a complex issue you encountered, detailing the steps you took to resolve it. Focus on your problem-solving skills and the lessons learned from that experience.

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What experiences do you have with parameter-efficient fine-tuning methods?

Discuss your familiarity with methods such as LoRA or QLoRA, and provide examples of projects where you implemented these techniques. Highlight the results and improvements achieved in model efficiency and accuracy.

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How do you approach collaboration with product teams at your previous roles?

Explain your collaborative skills, giving examples of how you’ve successfully worked with product teams to translate technical requirements into product features. Focus on the importance of teamwork in achieving common goals.

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What techniques do you use to evaluate model performance after fine-tuning?

Describe the evaluation metrics you consider essential and the methodologies you apply to compare fine-tuned models against base models. Emphasize your understanding of validation processes and how they contribute to improved models.

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How would you describe the importance of customer feedback in the fine-tuning process?

Discuss how customer feedback can guide product enhancements and help shape fine-tuning strategies. Reflect on your experiences of utilizing feedback to make iterative improvements in your projects.

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Full-time, on-site
DATE POSTED
April 2, 2025

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