Director, Enterprise Research
Scale AISan Francisco, CA; New York, NYPosted 31 March 2026
Tech Stack
Job Description
The Enterprise ML team works on the front lines of the AI revolution, partnering deeply with customers to identify high-impact business problems and build cutting-edge AI systems using Scale’s proprietary research, data, and infrastructure—unlocking domain expertise through high-quality data and expert feedback.
As Director of Enterprise ML, you will lead a world-class team of research scientists and engineers, define the research roadmap, and drive execution from early prototyping to deployment. You’ll thrive in a fast-moving environment, balancing deep technical leadership with people management, vision setting, and delivery.
This role is ideal for a leader who thrives in ambiguity, understands both frontier GenAI capabilities and their limitations, and is motivated by turning research into durable, production-ready systems.
What You’ll Do
Lead, mentor and grow a team of research scientists and engineers working on GenAI research initiatives (e.g., evaluation, post-training, agents, RL environments).
Define and drive a multi-year research roadmap: identify key scientific questions, set milestones, allocate resources, and ensure rigorous execution.
Collaborate cross-functionally with engineering, product, client-facing teams and external academic or industry partners to translate research into components, insights, and actionable outcomes.
Communicate compellingly: publish research, present at conferences, engage in open-source contributions, and represent the team externally.
Drive an inclusive, high-performing culture: help your team through technical challenges, provide growth opportunities, and attract top talent.
Stay deeply connected to the research community, understanding major trends, and helping set them.
Thrive in a high-energy, fast-paced startup environment and are ready to dedicate the time and effort needed to drive impactful results.
What We’re Looking For
Core Qualifications
8+ years of hands-on research experience (PhD or equivalent preferred) in machine learning, deep learning, generative models, agent/rl systems or related domains.
A strong track record of research excellence, including publications in top-tier ML/AI venues (NeurIPS, ICML, ICLR, ACL, etc.).
Experience and track of recording in landing major research impacts in a fast-paced environment
Experience leading or managing research teams. You’re excited to mentor, coach and develop talent.
Excellent written and verbal communication skills. You are able to articulate research ideas and outcomes to both technical and non-technical stakeholders.
Exceptional communication and stakeholder management skills, with the ability to influence executives, customers, and cross-functional partners
Nice to Have
Hands-on experience building and deploying agent-based, tool-augmented, or workflow-driven LLM systems in enterprise environments
Prior ownership of enterprise AI platforms, internal ML products, or customer-facing AI services at scale
Proven track record of partnering directly with enterprises to identify high-impact use cases and deliver measurable business outcomes
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 ma ... (truncated, view full listing at source)
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