Senior Machine Learning Engineer, Multimodality & Data
Company
Scribe
Location
San Francisco, CA
Type
Full Time
Job Description
About us
Scribe is where exceptional people come to do the best work of their careers. More than 90% of the Fortune 500 use Scribe to automatically create step-by-step guides and streamline knowledge sharing. We’re growing fast — since our founding in 2019, we’ve grown to over 2.5 million users across 450,000 businesses. Based in San Francisco, we’ve raised $55M in funding from top-tier investors and are honored to have been named Fortune’s Next Billion Dollar Startup in 2024. Join us in our mission to unleash and up-level the world’s know-how!
How we work
We are builders aspiring to master our crafts. We care deeply about our teammates and want to win, together. We embrace the following values:
- Accelerate impact
- Raise the bar
- Make our users heroes
- Clear is kind
- Rapid learning machine
- One team one dream
About this Role
Scribe is a productivity automation company based in San Francisco. We are seeking a highly motivated and skilled Machine Learning / Applied Research Engineer to join our team. You will work on cutting-edge research projects to build the future of agents (see our work on ScribeAgent here) and rapidly develop, test, and deploy groundbreaking AI-powered software to our millions of users. We are constantly pushing the envelope with cutting-edge results and are now looking for more talent to come join us and delight our millions of users.
You can expect to
- Explore cutting-edge techniques in the artificial intelligence field and translate these innovations into valuable features for our millions of users.
- Develop our multimodal capabilities (incl. vision, audio) end-to-end.
- Drive the design of a multimodal offline data pipeline for model training/benchmarking to push forward model performance.
- Design experiments and rapidly iterate to increase the performance of our multimodal LLMs, focusing on exploring and utilizing diverse data modalities.
- Spearhead our data collection, filtering, curation and synthesis efforts to improve model training and evaluation.
- Collaborate with our very talented engineering and product management team.
You could be a great fit if
- You have a relevant degree from a top ML program — preferably 2+ years of industry experience working deep in the weeds on hard ML problems.
- You have strong software engineering skills (including Python, Jupyter, etc.)
- You have strong expertise in computer vision and/or multimodal representation learning.
- You have experience exploring multimodal large language models that integrate various types of data such as text, image, video, and audio.
- You have great intuitions of fundamental ML concepts (e.g., fluent in thinking about overfitting, generalization, back-propagation, etc.)
- You have strong communication skills.
- You have a track record of successfully owning projects from start to finish.
Bonus
- You have startup experience (not required, but we build and move fast!)
- You have proven contribution to open-source projects or publications in machine learning, statistics, computer science or related technical fields.
- You have a deep understanding of systems engineering to build scalable solutions.
Full-Time US Employee Benefits Include
- Some of the nicest and smartest teammates you’ll ever work with
- Competitive salaries
- Comprehensive healthcare benefits
- Exciting and motivating equity
- Flexible PTO
- 401k
- Parental Leave
- Commuter Benefits (SF office employees)
- WFH Stipend
Compensation
$160-$200k USD base + equity + benefits. We consider several factors when determining compensation, including location, experience, and other job-related factors.
At Scribe, we celebrate our differences and are committed to creating a workplace where all employees feel supported and empowered to do their best work. We believe this benefits not only our employees but our product, customers, and community as well. Scribe is proud to be an Equal Opportunity and Affirmative Action Employer.
Date Posted
12/15/2024
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