Machine Learning Engineer - Fine Tuning

Posted 9 Days Ago
2 Locations
Mid level
Software
The Role
As a Machine Learning Engineer, you will fine-tune models, develop pipelines, and work with customers to implement effective solutions, focusing on advanced techniques like LoRA and distributed training.
Summary Generated by Built In

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 IVP, Spark Capital, Greylock, and Conviction, we’re trusted by leading enterprises and AI-driven innovators—including Descript, Bland.ai, Patreon, Writer, 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.

Top Skills

AWS
Axolotl
Azure
Fine-Tuning
GCP
Lora
Machine Learning
Pytorch Lightning
Qlora
Torch Tune
Transformers
Trl
Unsloth
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The Company
59 Employees
On-site Workplace

What We Do

At Baseten we provide all the infrastructure you need to deploy and serve ML models performantly, scalably, and cost-efficiently.

Get started in minutes, and avoid getting tangled in complex deployment processes. You can deploy best-in-class open-source models and take advantage of optimized serving for your own models.

We also utilize horizontally scalable services that take you from prototype to production, with light-speed inference on infra that autoscales with your traffic.

Best in class doesn't mean breaking the bank. Run your models on the best infrastructure without running up costs by taking advantage of our scaled-to-zero feature

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