Machine Learning Engineer/Applied Researcher

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 emerged from the brainstorming of three co-founders – Mark McQuade, Jacob Solawetz, & Brian Benedict – who envisioned a platform that would allow companies to use SLMs to fuel innovation while still retaining full control over their data and models. It was a vision based on deep knowledge of both the technical and business aspects of AI and machine learning, which they had gained via leading roles in companies including Hugging Face, Roboflow, and Tecton.

Upon Arcee.AI’s emergence from stealth in September 2023, the market immediately confirmed the need for their easy-to-use platform for creating performant and efficient custom LLMs, or what they call Small Language Models (SLMs). As they announced their Seed Round in January 2024, quickly followed by their Series A in July, they say what they’re most proud of is seeing their expanding customer base empowered by Arcee.AI-built SLMs – which are driving business value and innovation for enterprises across the globe every day.

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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.