Cantina is hiring a Machine Learning Engineer, Core Evaluations in Engineering (Europe).
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 their experience to lead our evaluation efforts and play a key role in the future growth of the evaluation team.
Key Responsibilities
- 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)
Perks & Compensation
The anticipated annual base salary range for this role is between $200,000-$220,000 (€170,000-€190,000). 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 days10 sick days15 company holidays2 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!
About Cantina
Cantina, created by the Spearbit team, is a premier Web3 cybersecurity and auditing platform that connects decentralized protocols with the world's top security researchers and independent whitehat auditors. Cantina provides crowdsourced security competitions, private guild audits, formal verification, and continuous monitoring to protect billions in on-chain capital. Built for fast-moving protocols that demand institutional-grade security, the platform streamlines vulnerability disclosure and remediation. The culture is deeply technical, security-centric, and focused on safeguarding open financial systems. Cantina streamlines smart contract audits by bringing together independent security researchers, formal verification specialists, and automated static analysis tools into a single collaborative security portal.