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Lead Machine Learning Research Engineer, Generative Ai

Company

Scale AI

Address San Francisco, CA, United States
Employment type FULL_TIME
Salary
Category Software Development
Expires 2023-07-12
Posted at 11 months ago
Job Description
Scale's Generative AI Data Engine powers the most advanced LLMs and generative models in the world through world-class RLHF/RLAIF, data generation, model evaluation, safety, and alignment.


As the Lead of the Generative AI team, you will be responsible for managing and leading a group of talented researchers and engineers. Your primary focus will be to leverage your expertise in LLMs, generative models, and other foundational models to create and execute an AI roadmap which will help Scale accelerate our customers' Generative AI initiatives forward. This is an exciting opportunity to work on cutting-edge technologies and collaborate with industry-leading professionals.


We are building a large hybrid human-machine system in service of ML pipelines for dozens of industry-leading customers. We currently complete millions of tasks a month and will grow to complete billions monthly.


You will:


  • Led a team of highly effective researchers and engineers. Provide guidance, mentorship, and technical leadership to a team of researchers and engineers working on Generative AI projects. Develop and evaluate methods for integrating machine learning into human-in-the-loop labeling systems to ensure high-quality and throughput labels for our customers.
  • Be able and willing to multi-task and learn new technologies quickly.
  • Work with massive datasets to develop both generic models as well as fine-tune models for specific products.
  • Work with customers and 3rd party research groups to understand their goals and define how we can enable them.
  • Build a scalable ML platform to automate our ML services, including automated model retraining and evaluation.
  • Work with product and research teams to identify opportunities for improvement in our current product line and for enabling upcoming product lines.
  • Implement and improve on state-of-the-art models developed internally and from the community and put them into production to solve problems for our customers and taskers.
Ideally you'd have:


  • 5+ years of experience using LLM, deep learning, deep reinforcement learning, or natural language processing in a production environment. Especially training foundational AI models through pre-training, fine-tuning, and RLHF.
  • Solid background in algorithms, data structures, and object-oriented programming
  • Experience working with cloud technology stack (eg. AWS or GCP) and developing machine learning models in a cloud environment
  • Degree in computer science or related field
  • Experience with MLOps and the automation of model training & evaluation
  • Deep appreciation for building high-quality, robust, reusable machine-learning software
  • Strong programming skills in Python, experience in PyTorch or Tensorflow
  • A vision for where the field should go and what Scale should do to enable it.
Nice to haves:


  • Graduate degree in Computer Science, Machine Learning or Artificial Intelligence specialization
  • Publication experience in the field or related topics.
  • Experience with model optimization techniques for both training and inference


The base salary range for this full-time position in our hub locations of San Francisco, New York, or Seattle, is $176,000 - $250,000. Compensation packages at Scale include base salary, equity, and benefits. The range displayed on each job posting reflects the minimum and maximum target for new hire salaries for the position, determined by work location and additional factors, including job-related skills, experience, interview performance, and relevant education or training. Your recruiter can share more about the specific salary range for your preferred location during the hiring process. Scale employees are also granted Stock Options that are awarded upon board of director approval. You’ll also receive benefits including, but not limited to: Comprehensive health, dental and vision coverage, retirement benefits, a learning and development stipend, and generous PTO. Additionally, this role may be eligible for additional benefits such as a commuter stipend.


About Us:


At Scale, we believe that the transition from traditional software to AI is one of the most important shifts of our time. Our mission is to make that happen faster across every industry, and our team is transforming how machine learning can build innovative products. Our products provide access to human-powered data for hundreds of use cases and are used by industry leaders such as Open AI, Lyft, Meta, GM, Samsung, Airbnb, NVIDIA, and many more. We’ve recently raised $325 million in Series E funding at a valuation of $7B+ and are expanding our team to accelerate the development of AI applications.


We believe that everyone should be able to bring their whole selves to work, which is why we are proud to be an inclusive and equal opportunity workplace. We are committed to equal employment opportunity regardless of race, color, ancestry, religion, sex, national origin, sexual orientation, age, citizenship, marital status, disability status, gender identity or Veteran status.


We are committed to working with and providing reasonable accommodations to applicants with physical and mental disabilities. If you need assistance and/or a reasonable accommodation in the application or recruiting process due to a disability, please contact us at [email protected]. Please see the United States Department of Labor's EEO poster and EEO poster supplement for additional information.


PLEASE NOTE: We collect, retain and use personal data for our professional business purposes, including notifying you of job opportunities that may be of interest. We limit the personal data we collect to that which we believe is appropriate and necessary to manage applicants' needs, provide our services, and comply with applicable laws. Any information we collect in connection with your application will be treated in accordance with our internal policies and programs designed to protect personal data.