Senior Applied Scientist
At a glance
Senior AI Research role at Outreach. Seattle · full-time · $140,000–$175,000 base.
aimlroles summary, based on the employer's posting.
What you'll do
- Build AI systems that turn voice streams and live transcripts into structured information, context signals, and useful insights
- Develop dependable, low-latency AI pipelines spanning distributed components and teams
- Prototype machine-learning and LLM approaches quickly to test solutions to ambiguous problems
- Improve performance across models and service orchestration to address key pain points
- Help shape customer-facing AI features through applied research and production engineering
What you bring
- At least two years implementing machine-learning and NLP systems, including LLM architectures and agentic workflows
- A master’s or doctoral degree in computer science, machine learning, or a related discipline
- Strong statistics and experiment-design skills, plus proficiency in Python and one of Java, Go, or C++
- Experience building and deploying cloud-based AI applications
Who this fits
This role suits a senior applied scientist who wants an even mix of ML research and production engineering, working with engineers and product managers. The position is based in Seattle and is hybrid.
From the employer
About the Team
The Dialog Understanding and Generation team is building the industry's most advanced agentic AI capabilities for revenue orchestration, the end-to-end coordination and execution of sales and customer engagement workflows. Our mission is to transform raw customer interactions and CRM data into structured intelligence that powers the entire revenue lifecycle.
We operate at the intersection of machine learning, LLM-powered systems, and cloud-scale engineering, driving both current and next-generation AI initiatives at Outreach.
We are a team of applied data scientists who thrive on solving complex scientific and engineering challenges. The work is roughly an even split between applied ML research and production engineering, so you should be excited about both. You'll collaborate closely with exceptional engineers and product managers in a fast-moving, high-impact environment, applying your creativity and expertise to build innovative solutions that push the boundaries of agentic AI.
Your Daily Adventures Will Include:
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Building AI-powered systems that process voice streams and real-time transcriptions to extract structured intelligence, contextual signals, and actionable insights.
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Engineering high-availability AI pipelines across distributed, cross-team components to ensure reliable, low-latency agentic workflows.
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Collaborating with stakeholders to ensure customers have the AI-driven capabilities and tools they need to succeed on the Outreach platform.
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Rapidly prototyping ML and LLM solutions to validate approaches and accelerate iteration on complex, ambiguous problems.
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Optimizing across the stack to maximize ROI for key pain points, from model architecture to service orchestration.
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Contributing to Outreach's most visible AI surfaces, shaping how customers experience our next-generation agentic AI capabilities.
Our Vision Of You:
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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
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Master’s Degree, or PhD, in relevant field such as computer science, machine learning, or related disciplines
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Strong foundation in statistics and experiment design, and passion for data are essential for success in this role
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Proficiency in Python, Java or Go or C++, along with strong software engineering skills
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Experience developing and deploying cloud-based AI applications
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Familiar with continuous-deployment projects
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A collaborative mindset and willingness to support and elevate teammates
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Ability to prioritize effectively and deliver incrementally in fast-moving environments.
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Ability to quickly learn new technologies, frameworks, and LLM-related tooling.
Preferred Qualifications:
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Experience with speech-to-text systems or real-time audio/voice processing.
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Familiarity with agentic AI frameworks or tool-use patterns in LLM systems.
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Experience building low-latency, high-availability ML serving infrastructure.
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Track record of taking ML models from prototype to production at scale.