Staff Applied Scientist - Knowledge Graphs & AI

Outreach is hiring a Staff Applied Scientist - Knowledge Graphs & AI for a remote role open to applicants in India. Level rates it ; you can apply on Outreach.

Level's read on this job

On Level's scale, this applied scientist role is Builds AI ●●●●: the job is building AI systems.
This is listed as a remote role.
The posting mentions Databricks, Python and PyTorch among its tools and skills.
Level classes it as a lead role.
Outreach has 22 other open roles on Level.

AI in this role

Design and build contextual knowledge graphs and AI reasoning systems to power sales execution and agentic workflows.

databrickspythonpytorchneo4jllms

machine-learningknowledge-graphsnlpapplied-sciencereasoning-systems

About Outreach Outreach, founded in 2014, is the only complete agentic AI platform for revenue teams. Outreach infuses agentic AI, conversation intelligence, and assistive AI to power hundreds of use cases across revenue motions. From new logo prospecting to expansions, deal acceleration, driving retention, and forecasting, Outreach AI automates workflows and frees sellers to focus on more strategic conversations and actions. Revenue leaders benefit from connected account visibility, performance insights, and higher forecasting accuracy across every GTM team. World leading enterprise organizations use Outreach to power their revenue teams, including Databricks, SAP, Siemens, and Verizon to name a few.

About the job:

We are looking for an Applied Scientist to join a dynamic and innovative AI platform team that is pushing the boundaries of what's possible in sales execution. If you are passionate about applying cutting-edge research in knowledge graphs and reasoning systems to real-world problems at scale, this is an exceptional opportunity to shape a core piece of Outreach's AI architecture from the ground up.
Our team is building a per-tenant contextual knowledge graph that captures the full complexity of each customer's sales environment: accounts, deals, contacts, rep behaviors, competitive landscape, and the signals buried in calls, emails, and CRM activity. This graph powers contextual reasoning across the platform, driving next-best-action recommendations, deal risk signals, coaching suggestions, and competitive intelligence. In this pivotal role, you will design the underlying representations, extraction pipelines, and reasoning layers that make this possible, working closely with cross-functional engineering and product teams to deliver innovative, scalable, and reliable AI capabilities with direct impact on revenue outcomes.

How we rate this

Staff Applied Scientist - Knowledge Graphs & AI at Outreach rates 90 out of 100 for how much of the daily work is AI. That makes it Builds AI (AI Level 4 of 4). The level is about AI in the job, not seniority.

Classification

Builds AI. The job is building AI systems.

●●●● Builds AI80 to 100
●●●○ Works on AI60 to 79
●●○○ Uses AI40 to 59
●○○○ Little AI0 to 39

Levels come from how often the tools, models and workflows of the role are named in the posting itself. Read the description and count the AI tools, models and workflows it names.

Key facts

Staff Applied Scientist - Knowledge Graphs & AI is an open job at Outreach.
Level rates this role AI Level 4 of 4: Builds AI.
The role is based in Hyderabad and is marked remote.
The job was posted on July 3, 2026.
Seniority: lead.
Applications are handled by Outreach. Apply on Outreach

Prepare for this job

Built from this posting and from similar jobs on Level.

Skills and AI tools this role asks for

Machine learningKnowledge GraphsNLPApplied ScienceReasoning SystemsDatabricksPythonPyTorch

Also common in applied scientist jobs at this level

Python Machine learning C++Java Deep learning LLM

Pay

Typical applied scientist job: $172k

This posting doesn't list pay. Use the typical applied scientist figure when pay comes up. See applied scientist jobs

Questions you could be asked

Tell me about a project where machine learning was part of your work. What did you do?
Tell me about a project where knowledge graphs was part of your work. What did you do?
What NLP problem have you worked on, and how did you measure whether it actually worked?
Tell me about a project where applied science was part of your work. What did you do?
Tell me about a project where reasoning systems was part of your work. What did you do?

Adapt your resume

List these exact terms on your resume: Machine learning, Knowledge Graphs, NLP, Applied Science, and Reasoning Systems. An applicant tracking system matches the wording, not the idea.
Attach one line of real, concrete experience to at least one of them. A tool named with nothing behind it rarely survives a human read.
Lead with what you built, trained or shipped. This role is judged on the AI system itself, not the tools around it.

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