At Yakoa, we protect some of the most prominent brands in the world by keeping unlicensed digital property away from Web 3 platforms. To handle the massive scale of digital content creation, our technology relies on large-scale data processing and recent advancements in the field of AI.About the role:
We’re hiring a versatile machine learning engineer to apply the latest developments from self-supervised learning across a variety of computer vision, NLP, and signal processing scenarios.
The successful candidate will be adept at uncovering the core innovations from recent AI publications, iterating through various experimental setups, and applying disciplined creativity to boost the performance of our AI models.
Working closely with a cross-functional team of engineers, data scientists, and product experts, you'll bring a consistent stream of new model architectures and incremental improvements to experimental accuracy to production, all while balancing the demands of our customer base with the advancements of the field. You'll be an integral part of a tight-knit team, designing and implementing the systems that power our products. Skill in abstract reasoning along with comfort with ambiguity are essential.
The ideal candidate is a quick learner who is passionate about staying up-to-date with the latest advancements in AI - both the deep learning innovations we apply to our ML architectures and the LLM capabilities that augment the way we work.Responsibilities
- Parse the latest research and translate their innovations into meaningful experiments, boosting the accuracy of Yakoa’s deep learning models
- Reason through customer-facing feedback on model performance, diagnosing the failure modes of production models, and searching for solutions
- Balance your priorities between synthetic data augmentation, hyperparameter optimization, and ensemble building
- Build ML models capable of massive-scale inference, where resource constraints are an ever-present design criteria
- Mentor ML engineers while serving as technical lead, contributing to and directing the execution of complex projects
- 5+ years working as a machine learning engineer or researcher
- Familiarity with state-of-the-art research in self-supervised learning, with a deep understanding of the latest optimizations to transformer architectures
- Experience stress-testing model performance with tooling built on cloud-backed environments like Google Cloud
- Proficiency in Python and PyTorch
- Proven analytical, communication, and organizational skills and the ability to prioritize multiple tasks at a given time
Exceptional candidates also have:
- Publications of AI research in reputable journals, related to the field of self-supervised learning or similar
- B2B software design experience and comfort navigating the full suite of feature development, through scoping, development, testing, and feedback integration
- Unlimited PTO
- Competitive compensation packages
- Remote hybrid work environment with flexible hours and team hubs in Vancouver and the San Francisco Bay Area
- Wellness packages for mental and physical health