Sesame
Research Scientist
San Francisco · Contract
Sponsorship not specifiedDetected 8 days ago
Machine LearningNLPComputer VisionResearch
About the role
- New and novel approaches are needed to realize all of our product goals.
- As a Machine Learning Scientist at Sesame, you are a research-oriented person with experience in NLP, Speech, and/or Computer Vision with a focus on deep learning.
Responsibilities
- Pick promising approaches from the literature to bet on, and create new approaches where necessary to achieve our unique goals.
- Sesame believes in a future where computers are lifelike - with the ability to see, hear, and collaborate with us in ways that feel natural and human.
Requirements
- Experience working independently in high-ambiguity environments.
- Bachelor's degree or higher in CS or related field.
- You are up to date on recent papers and approaches and you have the creativity and the intuition to come up with new solutions based on the application at hand.
Nice to have
- Masters/PhD desired.
- Experience working with products.
- Experience in a startup environment.
- Sesame is committed to a workplace where everyone feels valued, respected, and empowered.
- We welcome all qualified applicants, embracing diversity in race, gender, identity, orientation, ability, and more.
- We provide reasonable accommodations for applicants with disabilities.
- Contact careers@sesame.com for assistance.
- 401 (k) max employer match: 3.5% of compensation
Compensation
- 401 (k) max employer match: 3.5% of compensation
Benefits
- 100% employer-paid health, vision, and dental benefits for you and your dependents
- Unlimited PTO and sick time
- Flexible spending account with employer matching up to $1,650/year (medical FSA)
- Opportunity to share in the company's success with competitive stock options
- Benefits do not apply to contingent/contract workers.
- With this vision, we're designing a new kind of computer, focused on making voice agents part of our daily lives.
This listing is sourced directly from Sesame's careers page and normalized into a canonical job model.