Data Science - AI
The AI Evaluation Engineer will analyze training and evaluation datasets to identify distributional gaps, labeling inconsistencies, and long-tail opportunities; design and execute labeling campaigns including the development of golden datasets and annotation guidelines; build and maintain dashboards to track model accuracy, regression trends, and product-specific KPIs; investigate failure modes through prompt clustering, error taxonomy development, and user intent classification; operationalize feedback loops by mining product telemetry and human-in-the-loop reviews for signal and translate these into data-driven model improvement strategies; partner with engineers and product managers to run structured A/B tests and human evaluations for new models or features; support the development of scalable data and evaluation infrastructure for LLMs and agents; and work with product, engineering, and legal teams to create clear and transparent processes for handling customer data in AI training, fine-tuning, and evaluation.
Sr. Applied AI Engineer
As a Sr. Applied AI Engineer, you will build reusable AI products by acting as the product owner for your application area, designing, developing, and deploying robust, repeatable Generative AI agents that serve as configurable solutions for customers. You will partner with Solution and Forward Deployed Engineers during sales and implementation projects to understand customer needs, develop standard templates and reusable components to reduce time-to-activation, and solve core challenges. You will synthesize customer feedback to form a clear vision for your agents, iterate on solutions to solve concrete use cases at scale, and treat each agent as a product itself. Additionally, you will collaborate closely with the core product team to prioritize platform features that unblock application development and serve as an expert user consultant during new feature development.
Sr. Applied AI Engineer
As a Sr. Applied AI Engineer at Taktile, the responsibilities include building reusable AI products by acting as the product owner for application areas, designing, developing, and deploying robust generative AI agents as configurable solutions for customers. The role requires partnering with Solution and Forward Deployed Engineers during sales and implementation projects to understand customer needs in depth, and developing standard templates and reusable components to reduce activation time and address core challenges. The engineer must synthesize customer feedback into a clear vision for AI agents, iterating solutions to solve concrete use cases at scale, treating every agent as a product itself. Collaboration with the core product team is essential to prioritize platform features that support application development and acting as an expert user consultant during new feature development.
Software Engineer
Design and build the backend systems and services that power Sesame's product, including data models, APIs, and distributed systems. Write durable software focusing on scalability, reliability, and correctness rather than prototyping. Build and evolve frameworks and libraries for other engineers to use, emphasizing good software design. Own the full lifecycle of services, including schema design, implementation, deployment, performance tuning, and on-call responsibilities. Work with various data stores such as relational databases, NoSQL, queues, caches, and search indexes. Identify and resolve performance bottlenecks while considering cost, throughput, and latency. Architect systems where machine learning models are a key component but not the sole aspect, such as real-time audio pipelines, agentic orchestration, and stateful conversation systems. Identify opportunities to improve developer efficiency through prototyping tools or workflow improvements and collaborate with the infrastructure team to productionize them.
Staff Software Engineer, Anti-Abuse & Security
Design and implement LLM guardrails that detect abuse scenarios in AI-generated code and agent interactions. Build AI-powered detection systems that use LLMs to identify malicious patterns, classify threats, and automate response decisions. Build and operate abuse detection systems that identify phishing, cryptomining, account takeover, and financial fraud across millions of daily user actions. Design automated response mechanisms that enforce platform policies without manual intervention. Own the full abuse response lifecycle: detection, investigation, enforcement, and handling appeals alongside Support and Legal. Analyze attack patterns using BigQuery and Hex, turning investigation findings into new detection rules. Maintain and extend internal detection tools (Slurper, Netwatch) that continuously monitor user activity. Integrate and tune security scanners (SAST, SCA) in CI pipelines with tight performance SLAs. Track abuse trends, measure detection effectiveness, and adapt defenses as attack patterns evolve.
Agentic Finance Engineer
The Agentic Finance Engineer is responsible for designing, building, and maintaining a reliable financial data foundation using modern tools, covering revenue, AP/AR, procurement, close, strategic finance, and FP&A. They will partner closely with the data infrastructure team to build the financial data model, define canonical datasets, dimensional schemas, and transformation logic for Finance stakeholders. This role includes partnering with Finance leads to translate business requirements into technical architecture, building and maintaining dashboards and self-serve reporting tools to provide real-time visibility into key metrics. The engineer will own the Agentic Finance roadmap, prioritize use cases, and drive features from ideation to deployment, identifying high-value automation opportunities across Finance and corporate operations, and shipping solutions to eliminate manual work. They will build intelligent, reliable automation using agents, AI-powered tools, multi-step ETL jobs, and internal tooling that Finance teams use, such as lightweight apps, workflow automations, and AI-assisted processes. The engineer must enforce data integrity standards and testing practices to ensure auditability and reliability, ensure AI-assisted processes meet governance and controls standards with clear auditability, and champion a culture of data quality and documentation so that Finance teams trust and rely on the systems built.
Senior Manager, Data Science
As the Senior Manager of Data Science at Abridge, the responsibilities include building and managing a world-class team of data scientists, providing guidance, mentorship, and setting high standards. The role requires fostering a collaborative team culture focused on agency and clarity of thought, driving product strategy through data-driven insights across the product portfolio, and analyzing user behavior and product performance metrics. It involves partnering with product, strategy, and research teams to develop ROI frameworks for customers by ingesting real-time data to demonstrate impact. The role also includes collaborating with the research team on models and model evaluation, shaping evaluation frameworks, monitoring production performance, and defining clinical quality metrics. The Senior Manager must communicate data strategy, complex analyses, and insights to cross-functional partners and the executive team, structure the company's data strategy alongside Data Engineering by identifying data gaps and optimizing data use, make critical technical infrastructure decisions including tooling choices and technical standards, and build the data science organization to incorporate best practices and AI to accelerate data ingestion and insight generation.
Abuse Investigator (AI Self-Improvement Risk)
As an Abuse Investigator focused on AI Self-Autonomy and Agentic Risk on the Intelligence and Investigations team, you will be responsible for identifying and investigating cases where models exhibit autonomous or agentic behavior, including chaining capabilities, acting with increasing independence, or demonstrating patterns that may introduce safety risk. This includes detecting behaviors that are not explicitly intended, understood, or covered by existing safeguards. You will review leads, investigate model behavior, and identify cases where systems demonstrate agentic or autonomous patterns that introduce safety risks. You will detect and analyze behaviors such as multi-step planning, capability chaining, tool use, persistence, and workaround behavior. You will develop signals and tracking strategies to help proactively identify emerging agentic risk patterns across the platform. You will identify gaps in existing safeguards, evaluations, or monitoring systems and propose improvements. You will communicate investigation findings clearly to technical, policy, and leadership stakeholders. This role involves working in high-pressure environments and interacting with others effectively.
Senior Data Intelligence Engineer
The Senior Data Intelligence Engineer is responsible for building and maintaining high-fidelity dbt and SQL models that serve as the foundational data for complex, usage-based revenue models. They develop tools and permissions frameworks enabling 'Analyst Agents' to query data sources such as Athena, correlate Salesforce churn signals, and identify API latency issues. The engineer acts as the technical liaison with the Engineering/Infrastructure team to ensure data contracts are reliable and ready for autonomous agents. They partner with the Head of Data to ingest and transform thousands of hours of unstructured internal call audio into queryable insights for go-to-market teams using Deepgram’s own models. The role includes maintaining a culture focused on automating manual and repetitive SQL tasks through code and agent systems rather than legacy dashboards.
Lead Data Scientist
As the Lead Data Scientist, you will set the data strategy by defining what is measured, how it is measured, and establishing the metrics architecture that connects product usage, retention, monetization, and growth across the company. You will transform the data team into a product team by building internal data products and self-serve AI interfaces, automated reports, and tools for non-technical stakeholders. You will build the semantic layer, documentation, and context infrastructure to make the data warehouse AI-readable and accurate. Additionally, you will build AI-powered systems and automated pipelines to replace manual work within the data lifecycle, including dbt model generation, data quality monitoring, experiment analysis, and insight delivery. You will own product analytics and experimentation by partnering with Product, Engineering, and Design to design experiments, interpret results, and provide insights that guide product decisions. Your responsibilities also include driving growth and business intelligence by maintaining and evolving dashboards and reporting for Sales, Marketing, Customer Success, and leadership, ensuring metrics are visible, trusted, and actionable. Finally, you will scale the data team’s output through systems and AI-powered tooling to support company growth without increasing headcount linearly.
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