SlipstreamJobs tracks this role from the company's public career site. Apply directly on the employer's site.
Salary: USD 453,600 - 567,000 / annual
Scale is the leading data and evaluation partner for frontier AI companies, dedicated to advancing generative AI through frontier evaluations, post-training data science, agentic applications, and trustworthy AI oversight. Scale Labs is the company's frontier research effort tackling challenging problems in evaluation, agents, post-training, reasoning, safety, and alignment of advanced AI systems. The SPRL team focuses on safety and policy research to bridge AI research and global policymakers.
As VP of Research, you will lead Scale's ML Research function and build a world-class research organization that expands frontier AI capabilities while developing safeguards for responsible deployment. You will shape the technical direction of Scale's ML research, translate science into production-ready solutions, and partner with go-to-market, delivery, engineering, and customers to define the next era of AI.
Key responsibilities include: leading the ML Research function with accountability for delivery, quality, performance, and roadmap execution; building and scaling a global high-performing team with emphasis on initiative ownership and ML excellence; partnering directly with Fortune 100 customers and the GTM team to translate business needs into scalable technical solutions; collaborating with engineering, product, and delivery teams to drive cutting-edge research; shaping long-term vision and org design of the ML Research team aligned with business growth; managing and mentoring senior ML Research leaders while instilling a culture of accountability, speed, and results; making rigorous tradeoffs across technical and business dimensions backed by financial and operational insight; and representing ML Research in cross-functional planning cycles including headcount, budget, and strategic initiatives.
Ideal candidates have a strong track record leading ML Research teams in high-growth environments (ideally in ML, infrastructure, or complex systems); experience scaling research organizations across geographies; ability to operate with urgency and clarity, distilling priorities and moving teams from ambiguity to execution; experience partnering deeply with sales, delivery, and customer success teams; comfort operating both in the weeds and at the executive level; and credibility and curiosity in core technical areas, especially product engineering, infrastructure, or AI agent systems.
