Senior Applied Scientist
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Some ghost-posting signals
- open for 90 days (90+ without a fill is a strong ghost signal)
- 49 open roles at this company in 30 days (mass-hiring blitz)
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About the role
Your Daily Adventures Will Include:
Building AI-powered systems that process voice streams and real-time transcriptions to extract structured intelligence, contextual signals, and actionable insights.
Engineering high-availability AI pipelines across distributed, cross-team components to ensure reliable, low-latency agentic workflows.
Collaborating with stakeholders to ensure customers have the AI-driven capabilities and tools they need to succeed on the Outreach platform.
Rapidly prototyping ML and LLM solutions to validate approaches and accelerate iteration on complex, ambiguous problems.
Optimizing across the stack to maximize ROI for key pain points, from model architecture to service orchestration.
Contributing to Outreach's most visible AI surfaces, shaping how customers experience our next-generation agentic AI capabilities.
Our Vision Of You:
2 years of hands-on experience implementing machine learning and NLP systems, including LLM-based architectures, text classification, entity recognition, dialog, and agentic AI workflows
Master’s Degree, or PhD, in relevant field such as computer science, machine learning, or related disciplines
Strong foundation in statistics and experiment design, and passion for data are essential for success in this role
Proficiency in Python, Java or Go or C++, along with strong software engineering skills
Experience developing and deploying cloud-based AI applications
Familiar with continuous-deployment projects
A collaborative mindset and willingness to support and elevate teammates
Ability to prioritize effectively and deliver incrementally in fast-moving environments.
Ability to quickly learn new technologies, frameworks, and LLM-related tooling.
Preferred Qualifications:
Experience with speech-to-text systems or real-time audio/voice processing.
Familiarity with agentic AI frameworks or tool-use patterns in LLM systems.
Experience building low-latency, high-availability ML serving infrastructure.
Track record of taking ML models from prototype to production at scale.
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