Senior Software Engineer, GenAI
Scale AISan Francisco, CA; New York, NYPosted 7 April 2026
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 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 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 data pipeline and business outcomes
Architect and scale infrastructure capable of handling millions of tasks per week with high reliability and low latency
Partner cross-functionally with ML teams, Forward Deployed Engineers, and Product to ensure data quality 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 React, TypeScript, Node.js, Python, MongoDB, Elasticsearch, and/or Temporal
Strong product sense and ability to translate ambiguous problems into technical solutions
Comfortable working in a fast-paced, high-ownership environment with a bias toward execution
Excited to join a dynamic hybrid team based in San Francisco or New York City
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 rang ... (truncated, view full listing at source)
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