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NEXTMOVEFDE careers · United States

Senior Software Engineer, GenAI

Technologies

TypeScript · React · Node.js · MongoDB · Elasticsearch · Temporal · Python

About Scale AI

Data, evals, and GenAI platform for frontier labs, enterprises, and governments — the picks-and-shovels layer for production AI.

Series G

Job description

The full responsibilities and requirements are on the employer’s site.

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Source and classification

Internal deployment & tooling · Evidence for this classification:

how humanity will interact with AI. Our Approach As part of the interview process, you’ll be considered for opportunities across several teams within the GenAI Engineering organization, based on your interests, expertise, and business needs. Potential team placements include Allocation, Growth, Frontier Data, Trust & Safety, Pay, Operator, or Tasking Experience. Together, these teams power Scale’s AI data operations - from building high-impact datasets that push the boundaries of LLM capabilities, to optimizing contributor onboarding and incentives, to safeguarding data integrity through advanced trust, safety, and security measures. They work at the intersection of ML, operations, and analytics to ensure we deliver the highest-quality data at scale. Responsibilities: Design, build, and maintain robust, scalable systems across the full stack, including front-end, back-end, and
More from the job description

About Scale At Scale AI, our mission is to accelerate the development of AI applications. For 8 years, Scale has been the leading AI data foundry, helping fuel the most exciting advancements in AI, including: generative AI, defense applications, and autonomous vehicles. With our recent Series F round, we’re accelerating the abundance of frontier data to pave the road to Artificial General Intelligence (AGI), and building upon our prior model evaluation work with enterprise customers and governments, to deepen our capabilities and offerings for both public and private evaluations. About Data Engine Our Generative AI Data Engine powers the world’s most advanced LLMs and generative models through world-class RLHF (Reinforcement Learning with Human Feedback), 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. Our Approach As part of the interview process, you’ll be considered for opportunities across several teams within the GenAI Engineering organization, based on your interests, expertise, and business needs. Potential team placements include Allocation, Growth, Frontier Data, Trust & Safety, Pay, Operator, or Tasking Experience. Together, these teams power Scale’s AI data operations - from building high-impact datasets that push the boundaries of LLM capabilitie [... source excerpt omitted ...] rity through advanced trust, safety, and security measures. They work at the intersection of ML, operations, and analytics to ensure we deliver the highest-quality data at scale. Responsibilities: Design, build, and maintain robust, scalable systems across the full stack, including front-end, back-end, and infrastructure layers Implement high-impact features using modern technologies such as TypeScript, React, Node.js, MongoDB, Elasticsearch, and Temporal Collaborate closely with internal operators (your users are your neighbors) to identify bottlenecks and ship fast, pragmatic solutions Own core systems critical to our contributor platform, with direct impact on Scale’s GenAI da [... source excerpt omitted ...] ality and operational excellence Contribute to a strong engineering culture while setting best practices for teammates through mentorship, code reviews, and process improvements Requirements: 5+ years of software engineering experience, ideally in high-growth, product-focused environments Proven track record of shipping production systems at scale Drive reliability and performance across critical infrastructure systems, ensuring our platforms scale predictably and operate with high availability. Strong technical depth in one or more areas: front-end frameworks, distributed systems, data infrastructure, or developer tooling Experience working across the stack, ideally with Reac

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