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 Associate Applied Scientist to join a dynamic and innovative AI platform team. If you are passionate about applying machine learning to knowledge graphs and reasoning systems at scale, this is an opportunity to build core components of Outreach's per-tenant knowledge graph while developing deep expertise under the guidance of senior scientists.
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.
This role is ideal for someone with strong ML fundamentals who wants to build deep expertise in knowledge graphs and applied NLP in a fast-moving product environment.

Your Daily Adventures Will Include

Key Responsibilities:

Knowledge Graph Design & Construction: Design and implement entity resolution and ontology population within established graph schemas. Write and optimize queries for graph traversal and feature extraction. Own data quality for assigned domains.
Information Extraction: Build pipelines that extract structured knowledge from unstructured conversational and document data (sales calls, emails, CRM notes), including coreference resolution, relation extraction, and event detection. Run experiments to compare approaches and improve accuracy metrics.
Contextual Reasoning & Recommendation: Implement graph traversal logic and feature queries that feed downstream scoring signals. Build and maintain features for deal risk, next-best-action, or coaching recommendation surfaces.
Representation Learning: Train and evaluate link prediction and node classification models using established graph embedding methods. Implement evaluation pipelines and track model performance over time.
Domain Modeling: Translate sales concepts, such as deal stages, buyer engagement patterns, rep behaviors, and account health, into graph nodes and relationships under the guidance of senior scientists. Contribute to ontology design and documentation.
Cross-functional Collaboration: Work with software engineers to deploy models and pipelines into production. Write clean, tested code. Monitor system health and respond to incidents. Participate in code review and design discussions.

Qualifications

Our Vision of You:

PhD in a relevant field such as Computer Science, NLP, Machine Learning, or a related discipline with a focus on knowledge representation and reasoning, information extraction and relationship extraction, graph neural networks, recommendation systems, or conversation AI and dialogue systems. MS + 2 years of relevant experiecne will also be considered.
Solid engineering fundamentals. You can write production-quality code, not just prototype notebooks. You can write clean, tested Python code. Experience with graph databases or query languages (e.g., Neo4j, SPARQL, Cypher).
Demonstrated ability to build and evaluate ML models. You've trained models, measured performance using appropriate metrics, and iterated on results.
A track record of building things: whether that's research prototypes that went beyond the paper, open-source contributions, or side projects that required real systems thinking. You understand the gap between a research prototype and a reliable production system, such as monitoring, data drift, latency, and operational excellence.
Strong Ownership: Take end-to-end responsibility for research and model development initiatives, from problem formulation and data analysis through experimentation, production deployment, and ongoing performance monitoring, driving outcomes with minimal oversight.
Good communication skills. You can explain technical concepts to engineers and product managers.
Eager to learn. You are excited to develop deep expertise in knowledge graphs and applied NLP under the mentorship of senior scientists.

Nice To Have

Hands-on experience applying knowledge graphs or graph-based learning methods to real-world data in a production setting.
Strong fundamentals in at least two of: knowledge graph construction, information extraction, graph neural networks, or recommender systems.
Experience working with large-scale unstructured text data (conversational transcripts, email, or similar)
Experience with probabilistic graphical models, conversational AI, or sales/revenue domain data
Published research at top-tier venues

Why Join Us

Greenfield Architecture: Shape the design of a core AI system from the ground up, with the latitude to make foundational technical decisions that define the platform.
Depth That Matters: This role genuinely requires PhD-level thinking; you will tackle problems in entity resolution, temporal reasoning, and graph learning that demand it.
Applied Impact: Work with real production feedback loops and millions of sales interactions, not just benchmarks; see your models change how thousands of teams sell.
High Leverage, Low Bureaucracy: Join a small, senior team where your contributions are visible, your ideas ship fast, and you have direct access to leadership.
Career Growth: Opportunity to lead initiatives and mentor engineers.

Why You'll Love It Here

Highly competitive salary
25 days annual vacation time + sick time and casual leave
Group medical policy coverage available to employees and up to 5 eligible family members
OPD benefit covered up to INR 10,000
Life insurance and personal accident insurance at 3x annual CTC
26 weeks of maternity leave pay, and 15 days of paternity leave pay
Opportunity to be part of company success via the RSU program
Diversity and inclusion programs that promote employee resource groups like OWN+ (Outreach Women's Network), Adelante (Latinx community), OBX (Outreach Black Connection), Mosaic (AAPI community), Pride (LGBTQIA+), Gender+, Disability Community, and Veterans/Military
Employee referral bonuses to encourage the addition of great new people to the team
Fun company and team outings because we play just as hard as we work

Outreach is an equal opportunity employer. All applicants will be considered for employment without attention to race, color, religion, sex, sexual orientation, gender identity, national origin, veteran or disability status.

Our success is reliant on building teams that include people from different backgrounds and experiences who can elevate assumptions and ideas with fresh perspectives. We're dedicated to hiring the whole human, not just a resume. To that end, we look for a diverse pool of applicants-including those from historically marginalized groups. We would like to invite you to apply even if you don't think you meet all of the requirements listed below. We don't want a few lines in a job description to get between us and the opportunity to meet you.

We may use artificial intelligence (AI) tools to support parts of the hiring process, such as reviewing applications, analyzing resumes, or assessing responses and identifying potential inconsistencies or verification signals in application materials based on available information. These tools assist our recruitment team but do not replace human judgment. Final hiring decisions are ultimately made by humans. If you would like more information about how your data is processed, please contact us.

Senior Applied Scientist - Knowledge Graphs & AI