Suggested rewrite: Led a cross-functional initiative that improved [business outcome] by [measurable result], demonstrating experience relevant to this role...
Machine Learning Engineer, Core Evaluations
- Salary
- Undisclosed
- Department
- Data Science & Analytics
- Employment
- Full Time
- Experience
- Senior
- Published
- Apply before
- 10 Dec 2026
- Listing views
- 49
- Application actions
- 3
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About this role.
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)
Compensation:
When determining compensation, a number of factors will be considered, including skills, experience, job scope, location, and competitive compensation market data.
Benefits for U.S.-based roles:
Competitive salary and generous company equity
Medical, dental, and vision insurance – 99.99% of premiums covered by Cantina
42 days of paid time off, including:
15 PTO days
10 sick days
15 company holidays
2 floating holidays
Generous parental leave & fertility support
401(k) retirement savings plan
Lifestyle spending account – $500/month to use however you’d like
Complimentary lunch and snacks for in-office employees
One Medical membership, and more!
Annual salary information is not provided for this position. Explore salary ranges for similar roles in our Salary Directory ›
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Sample interview questions
I would start by translating product and research goals into objective and subjective metrics, such as intelligibility, speaker similarity, prosody, latency, and perceived naturalness. I would build a representative test suite, establish reproducible inference conditions, use statistical confidence intervals, and supplement automated metrics with blinded listener studies.
Listener preferences can vary because of task framing, audio equipment, language background, fatigue, and inconsistent rater quality. I would use randomized and blinded samples, clear rubrics, qualification tests, quality-control items, balanced assignments, and sufficient sample sizes with predefined statistical analysis.
I would segment results by speaker demographics, noise conditions, accents, domains, utterance length, and vocabulary type to identify where the models differ. I would also inspect substitution, deletion, and insertion patterns, evaluate latency and robustness, and use paired statistical tests to determine whether observed differences are meaningful.
I would work with product and research stakeholders to define the specific qualities behind naturalness, such as turn-taking timing, prosody, emotional consistency, pronunciation, and interruption handling. I would then pair targeted automated proxies with structured human-evaluation questions and validate that the metrics correlate with user preference and product outcomes.
I would first map existing models, datasets, release processes, and quality risks with the model, data, and product teams. Next, I would prioritize a minimum viable evaluation suite and reporting dashboard for the highest-impact models, establish experiment and dataset versioning, and propose a roadmap for scaling user studies, automated regression testing, and team capabilities.
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