Machine Learning Enginer, Core Evaluations

Cantina; United States; Remote; USD 140,000 - 210,000; Full time

Job summary

Cantina Labs seeks an experienced Machine Learning Engineer to focus on audio model evaluation, specializing in speech generation and recognition. You will design comprehensive evaluation pipelines for development and production and build automated dashboards to report results.

As a founding member of the evaluation team, you will lead efforts, collaborate with model, data, and product teams, and help shape the future growth of Cantina's evaluation capabilities.

Qualifications

• Proven ability to design metrics that capture model performance.
• Experience designing user studies on platforms like Mechanical Turk.
• Hands-on experience training and fine-tuning models for evaluation.
• Strong statistical knowledge for comparing results and supporting decisions.
• Excellent software engineering and programming abilities.
• Experience training ASR and TTS models.
• Experience on ML teams handling large-scale datasets and models.

Responsibilities

• Design evaluation pipelines for development and production environments.
• Create user studies to evaluate subjective model aspects.
• Translate requirements into measurable evaluation metrics.
• Develop automated dashboards to monitor model performance and compare results.
• Train new models to capture evolving evaluation metrics.
• Collaborate with the model team to improve models based on results.
• Work with data teams to determine data needed to improve performance.
• Coordinate with product managers to ensure correct measurement of requirements.
• Help grow and lead the evaluation team as a founding member.

Skills

Metric design

User studies MTurk

Model training

Statistical analysis

Engineering skills

ASR training

Large-scale ML

Job description

About Cantina:

Cantina Labs is a social AI company, developing a suite of advanced real-time models that push the boundaries of expression, personality, and realism. We bring characters to life, transforming how people tell stories, connect, and create. We build and power ecosystems. Cantina, our flagship social AI platform, is just the beginning.

If you’re excited about the potential AI has to shape human creativity and social interactions, join us in building the future!

About the Role:

We are seeking an experienced Machine Learning Engineer (MLE) to focus on audio model evaluation, specifically for speech generation and recognition models.

This role involves designing and developing comprehensive model evaluation pipelines for both development and production environments, as well as creating automated dashboards for reporting evaluation results.

As the founding member of our evaluation team, the ideal candidate is expected to leverage their experience to lead our evaluation efforts and play a key role in the future growth of the evaluation team.

What You’ll Do:

• Designing model evaluation pipelines for models in development and production

• Designing user studies for subjective model evaluations.

• Converting requirements into measurable metrics.

• Designing and developing automated evaluation dashboard to see model performances and compare results.

• Training new models to capture new and different evaluation metrics.

• Communicating with the model team to help design better models based on the evaluation results.

• Communicating with the data team to help decide the type of data necessary to improve model performance.

• Communication with the product-manager to make sure product requirements are correctly measured.

• Help grow the evaluation team as the founding member.

• Lead the evaluation team in the future.

What You’ll Bring:

• Strong experience and intuition for designing metrics that capture model performance.

• Strong experience with designing user studies on Mechanical Turk or similar platforms. .

• Strong experience with model training and fine-tuning for model evaluation.

• Strong statistical knowledge and experience to statistically compare evaluation results and take decisions.

• Very strong engineering and programming skills.

• Experience with training ASR, TTS models.

• Experience at ML teams working on large-scale machine learning problems. (>3B models with >1m hours of data)