About the role:

As a Machine Learning Engineer/Applied Researcher, you will be at the forefront of our efforts to train and fine-tune Large Language Models. You will work closely with our Research and Engineering teams to develop, implement, and optimize training strategies that enhance the performance and capabilities of our LLMs. Your expertise in modern supervised fine-tuning techniques will be crucial in pushing the boundaries of what our models can achieve.

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What you’ll do:

• Develop and implement supervised fine-tuning techniques such as instruct tuning, DPO, KTO, and other advanced methods.
• Train and fine-tune Large Language Models to improve their performance, accuracy, and generalization capabilities.
• Collaborate with cross-functional teams to design and execute experiments that validate and benchmark model performance.
• Optimize training pipelines for efficiency and scalability, ensuring the best use of computational resources.
• Stay current with the latest research and developments in the field of Machine Learning and AI, and incorporate best practices into our training processes.
• Contribute to the development of tools and frameworks that facilitate efficient model training and merging.
• Document research findings and technical processes, and communicate results to both technical and non-technical stakeholders.

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What we’re seeking:

• Master’s or PhD in Computer Science, Machine Learning, Artificial Intelligence, or a related field.
• Proven experience in training Large Language Models and applying modern supervised fine-tuning techniques.
• Strong programming skills in Python and familiarity with Machine Learning frameworks such as TensorFlow, PyTorch, or similar.
• Experience with instruct tuning, DPO, KTO, or other advanced training methods.
• Solid understanding of Machine Learning algorithms, model optimization, and performance evaluation metrics.
• Ability to design and conduct experiments, analyze results, and iterate on model improvements.
• Experience with distributed training environments and large-scale data processing
• Familiarity with MLOps practices and tools for managing Machine Learning workflows.
• Prior experience in a startup environment or a fast-paced, dynamic work setting.
• Contributions to the AI/ML research community through publications or open-source projects.
• Excellent problem-solving skills and the ability to work effectively in a collaborative startup environment.
• Strong communication skills, with the ability to explain complex technical concepts to both technical and non-technical stakeholders.

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About Arcee.AI

Arcee.AI was founded on a mission of empowering companies to productionalize LLMs, broadening access to world-class LLM technology, and furthering the frontiers of Generative AI research. We are firmly committed to the values of the Open Source community in particular through our work with our arcee-ai/mergekit repository.

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Equal Opportunity

We are an Equal Opportunity Employer, offering equal opportunity to all regardless of race, religion, gender identity, sexual orientation, age, citizenship, marital status, disability, and more. We would like to remind candidates that the listed qualifications for each role are not hard requirements, and we encourage them to apply if they feel they would be a good fit.

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Compensation

We offer competitive salaries, equity, and benefits. We base our salaries on location, role, and level as well as consideration of the candidate’s experience and overall qualifications.

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