Lead Machine Learning Research Engineer, Generative AI

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.

• 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.

• Work with product and research teams to identify opportunities for improvement in our current product line and for enabling upcoming product lines.

• 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.

• Be able and willing to multi-task and learn new technologies quickly.

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.

• A vision for where the field should go and what Scale should do to enable it.

• Strong programming skills in Python, experience in PyTorch or Tensorflow

• Experience with MLOps and the automation of model training & evaluation

• Experience working with cloud technology stack (eg. AWS or GCP) and developing machine learning models in a cloud environment

• Solid background in algorithms, data structures, and object-oriented programming

• Deep appreciation for building high-quality, robust, reusable machine-learning software

• Degree in computer science or related field

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.