Machine Learning Engineer, Violations

at  Scale AI

About Job
Software is eating the world, but AI is eating software. We live in unprecedented times – AI has the potential to exponentially augment human intelligence. Every person will have a personal tutor, coach, assistant, personal shopper, travel guide, and therapist throughout life. As the world adjusts to this new reality, leading platform companies are scrambling to build LLMs at billion scale, while large enterprises figure out how to add it to their products. To make them safe, aligned and actually useful, these models need human eval and reinforcement learning through human feedback (RLHF) during pre-training, fine-tuning, and production evaluations. This is the main innovation that’s enabled ChatGPT to get such a large headstart among competition.
At Scale, our Generative AI Data Engine powers the most advanced LLMs and generative models in the world through world-class RLHF, human data generation, model evaluation, safety, and alignment. The data we are producing is some of the most important work for how humanity will interact with AI.
We’re looking for Machine Learning Engineers to join our team. In this role, you’ll be given the opportunity to build cutting-edge models to make an impact in the AI industry and meaningfully drive millions of dollars in revenue. You’ll also get widespread exposure to the forefront of the AI race as Scale sees it in enterprises, startups, governments, and large tech companies.
The ideal person is a natural entrepreneurial engineer who can take an ambiguous scope and lead the execution of outcomes, doing what it takes to hit them incl coding, defining requirements, proposing solutions, etc. We strongly believe the best engineers own outcomes and deeply understand customer problems. This tweet by Greg Brockman summarizes it well:
https://twitter.com/gdb/status/1514291063233474560
You’re excited about solving customer problems, and you pick the technologies and tactics that balance speed, function, and long-term robustness.

You will:

• Develop state-of-the-art models to detect and combat fraud, cheating, and spamming in the GenAI ecosystem. Your work will play a crucial role in maintaining the trust and reliability of our AI solutions.
• Design and implement features that elevate model performance, ensuring our solutions stay ahead of emerging threats and challenges.
• Define and drive key performance indicators that directly influence business success.
• Assess technical tradeoffs with a critical eye, making decisions that balance innovation, efficiency, and practicality.
• Uphold coding standards and architectural principles by crafting clean, well-documented, and modular code.
• Actively engage in code reviews, offering constructive feedback, and actively contributing to continuous improvement efforts across the engineering department.

Ideally you’d have:

• Strong understanding of machine learning approaches and algorithms
• Able to build out ML pipeline from feature generation to model deployment
• Able to prioritize duties and work well on your own
• Ability to work with both internal and external partners
• Skilled at solving open ambiguous problems
• Strong collaboration skills

Nice to haves:

• Have experience with AI platforms and technologies, including generative models and LLMs.
• Experience building ML infrastructure.

Compensation packages at Scale for eligible roles 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. Scale employees in eligible roles are also granted equity based compensation, subject to Board of Director approval. Your recruiter can share more about the specific salary range for your preferred location during the hiring process, and confirm whether the hired role will be eligible for equity grant. 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.
Please reference the job posting’s subtitle for where this position will be located. For pay transparency purposes, the base salary range for this full-time position in the locations of San Francisco, New York, Seattle is:

Please refer the Job description for details