At a Glance
- Tasks: Design and develop evaluation pipelines for cutting-edge audio models in speech generation and recognition.
- Company: Join Cantina Labs, a pioneering social AI company transforming storytelling and connection.
- Benefits: Competitive salary, flexible work options, and opportunities for professional growth.
- Other info: Dynamic environment with a focus on innovation and collaboration.
- Why this job: Be a founding member of the evaluation team and shape the future of AI technology.
- Qualifications: Strong experience in model evaluation, user studies, and programming skills required.
The predicted salary is between 60000 - 80000 € per year.
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.
About the Role
We are seeking an experienced Machine Learning Engineer 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, you will lead our evaluation efforts and shape the future growth of the evaluation team.
What You’ll Do
- Design model evaluation pipelines for models in development and production.
- Design user studies for subjective model evaluations.
- Convert requirements into measurable metrics.
- Develop automated evaluation dashboards to visualize model performance and compare results.
- Train new models to capture new and different evaluation metrics.
- Communicate with the model team to help design better models based on evaluation results.
- Communicate with the data team to determine the type of data necessary to improve model performance.
- Communicate with the product manager to ensure 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 designing user studies on Mechanical Turk or similar platforms.
- Experience with model training and fine‑tuning for model evaluation.
- Strong statistical knowledge and experience in statistically comparing evaluation results and making decisions.
- Very strong engineering and programming skills.
- Experience training ASR and TTS models.
- Experience at ML teams working on large‑scale machine learning problems (e.g., >3B parameters with >1M hours of data).
Machine Learning Enginer, Core Evaluations employer: Cantina Labs
Cantina Labs is an exceptional employer, offering a dynamic work culture that fosters innovation and creativity in the field of social AI. As a Machine Learning Engineer, you will have the unique opportunity to lead the evaluation team from its inception, driving meaningful projects that shape the future of storytelling and connection. With a commitment to employee growth, collaborative teamwork, and cutting-edge technology, Cantina Labs provides a rewarding environment for those looking to make a significant impact in their careers.
StudySmarter Expert Advice🤫
We think this is how you could land Machine Learning Enginer, Core Evaluations
✨Tip Number 1
Network like a pro! Reach out to folks in the industry, especially those at Cantina Labs. A friendly chat can open doors that a CV just can't.
✨Tip Number 2
Show off your skills! Create a portfolio showcasing your model evaluation projects. This is your chance to demonstrate your expertise in designing metrics and user studies.
✨Tip Number 3
Prepare for interviews by brushing up on your statistical knowledge and programming skills. Be ready to discuss how you've tackled large-scale machine learning problems in the past.
✨Tip Number 4
Apply through our website! It’s the best way to ensure your application gets noticed. Plus, we love seeing candidates who take the initiative to connect directly with us.
We think you need these skills to ace Machine Learning Enginer, Core Evaluations
Some tips for your application 🫡
Tailor Your CV:Make sure your CV is tailored to the Machine Learning Engineer role. Highlight your experience with model evaluation, user studies, and any relevant projects that showcase your skills in designing metrics and dashboards.
Craft a Compelling Cover Letter:Your cover letter is your chance to shine! Use it to explain why you're passionate about AI and how your background aligns with our mission at Cantina Labs. Don’t forget to mention specific experiences that relate to the job description.
Showcase Your Technical Skills:We want to see your engineering and programming prowess! Include examples of your work with ASR and TTS models, as well as any large-scale machine learning problems you've tackled. This will help us understand your technical depth.
Apply Through Our Website:Don’t forget to apply through our website! It’s the best way for us to receive your application and ensures you’re considered for the role. Plus, it shows you’re serious about joining our team!
How to prepare for a job interview at Cantina Labs
✨Know Your Models Inside Out
Make sure you have a solid understanding of the audio models you'll be working with, especially in speech generation and recognition. Brush up on the latest advancements in ASR and TTS technologies, as well as any relevant metrics used to evaluate their performance.
✨Prepare for Technical Questions
Expect to dive deep into technical discussions during your interview. Be ready to explain how you would design model evaluation pipelines and user studies. Practise articulating your thought process clearly, as this will showcase your engineering skills and problem-solving abilities.
✨Showcase Your Statistical Knowledge
Since strong statistical knowledge is crucial for this role, prepare to discuss how you've used statistics to compare evaluation results in past projects. Bring examples of how you've made data-driven decisions based on your analyses.
✨Communicate Effectively
As a founding member of the evaluation team, communication will be key. Be prepared to discuss how you would collaborate with model and data teams, as well as product managers. Highlight any past experiences where effective communication led to successful project outcomes.
