Software Data Engineer
The Software Data Engineer will collaborate with Machine Learning, Full-stack engineers, and Science teams to address complex document mining challenges and enhance the capture and modeling of scientific experiments. The role involves scaling data pipelines to transition data quickly and reliably from research to the platform, working with both semi-structured and unstructured data sources. Responsibilities include defining and applying best practices for a broad range of cloud-based technologies, architecting and maintaining robust data pipelines that ingest diverse data sources and use large language models (LLMs) for high-fidelity entity extraction into structured formats. The engineer will implement evaluation frameworks to monitor accuracy, drift, and hallucination rates of extraction models within production pipelines. They will lead or consult on engineering design proposals aligned with the unified Platform Stream roadmap, make independent technical decisions based on the business context, proactively identify and implement project improvements, respond urgently to operational issues, own issue resolution within their responsibility scope, and challenge current practices by proposing new technologies or working methods.
Data Scientist, Preparedness
Evaluate and improve mitigation systems, including classifiers and detection pipelines across domains such as biosecurity, cybersecurity, and emerging risk areas. Diagnose false positives and false negatives through deep error analysis, root cause investigation, and provide clear recommendations for mitigation adjustments. Build monitoring and measurement frameworks to track mitigation effectiveness over time and across user segments and use cases. Identify trends in over-blocking versus under-blocking, quantify customer impact, and propose prioritized interventions. Develop insights from customer feedback, complaints, and usage patterns to detect shifts in adversarial behavior and system failure modes. Expand risk monitoring into new areas including cybersecurity threats and model loss-of-control or sabotage scenarios in partnership with domain experts. Communicate results to technical and executive stakeholders with clear narratives, decision-ready metrics, and clear tradeoffs.
Senior Data Scientist - Data Foundations & AI
As a Senior Data Scientist on the Data Foundation & AI team at Plaid, you will define and operationalize quality measurement across enrichment and AI systems to ensure products deliver reliable and impactful outcomes for customers. You will own and evolve evaluation frameworks, investigate customer-facing quality issues, and build automation to accelerate iteration and improvement cycles. You will partner closely with product and engineering teams to use data-driven insights for informing roadmap decisions and raising quality standards for AI-powered experiences. Responsibilities include defining quality metrics that shape product direction, turning ambiguous customer complaints into structured investigations, building scalable evaluation systems, measuring AI system effectiveness in a principled way, automating workflows using modern AI tools, and influencing roadmap decisions through data.
Senior Software Engineer
Own full lifecycle management of advertising systems, from experimentation through deployment and continuous enhancement. Design, implement, and deploy data-driven algorithms and computational models for intelligent advertising platforms. Build scalable, high-performance software components that enable content personalization for publishers and brands. Monitor and evaluate performance of deployed models to ensure high system reliability. Analyze performance data and implement improvements to optimize accuracy and efficiency. Conduct ongoing research into emerging algorithms and data processing techniques, aligning solutions with latest academic and industry advancements. Proactively integrate new methodologies and technologies to strengthen and expand system capabilities. Perform data wrangling and preprocessing using SQL and related tools to prepare structured training data. Ensure data integrity and usability through best practices in validation, error handling, and resource management.
Head of Decision Intelligence
The Head of Decision Intelligence at Deepgram is responsible for architecting and deploying AI-augmented intelligence systems that go beyond static dashboards to proactively reason across silos and provide automated root-cause analysis and actionable recommendations. They work closely with the Tech/Engineering team to co-evolve the data stack, define the semantic layer, and establish data contracts to make the infrastructure agent-ready and highly reliable. They leverage Deepgram’s Voice AI models to ingest and structure thousands of hours of internal calls, turning unstructured audio into queryable insights for functional teams. The role includes acting as the primary data partner to the CEO and Board, defining the source of truth for complex, usage-based revenue models, and providing analytical support for long-term strategy including pricing, packaging, distribution, market entry, and capital allocation. Additionally, the role entails recruiting and leading a lean, elite team of AI-native engineers, promoting a culture of automation and eliminating manual repetitive SQL tasks. Initially, the role involves hands-on work building AI-augmented analytical workflows and critical models while simultaneously growing the team and collaborating closely with Product, Sales, and Finance to ensure leadership has precise insight into NRR, unit economics, and growth loops.
Senior Data Engineer
Lead the end-to-end design and delivery of scalable, secure, and intelligent data products and solutions that support HackerOne's transformation into an AI-first organization. Partner across business and engineering teams to identify high-leverage opportunities for automation, integration, and system modernization. Drive the architecture and execution of platform-level capabilities, leveraging AI and modern tooling to reduce manual effort, improve decision-making, and increase system resilience. Provide technical leadership to internal engineers and external development partners, ensuring design quality, operational excellence, and long-term maintainability. Shape and contribute to incident and on-call response strategy, playbooks, and processes, focusing on building systems that fail gracefully and recover quickly. Act as a multiplier to mentor other engineers, advocate for technical excellence, and promote a culture of innovation, curiosity, and continuous improvement. Champion effective change management and enablement, ensuring systems are adopted, understood, and evolved.
AI/Machine Learning Engineer Intern
As an AI/Machine Learning Engineering Intern, you will contribute to building intelligent product experiences that help students discover and secure opportunities. Your work will span search, recommendations, matching, and other discovery systems that power job exploration on Handshake. You will partner with senior engineers and data scientists to develop machine learning models that improve user experience, build Agentic pipelines/workflows to improve the Handshake student/employer user experience, contribute to experimentation, model evaluation, and performance monitoring. Additionally, you will participate in technical discussions, brainstorming sessions, and team reviews, and document methodologies and findings to support knowledge sharing and long-term system improvements.
Data Scientist, Integrity Measurement
The data scientist will own measurement and quantitative analysis for a group of severe, actor- and network-based usage harm verticals. They will develop and implement AI-first methods for prevalence measurement and other productionised safety metrics, build metrics suitable for goaling or A/B tests when prevalence or other top line metrics are not appropriate, and own dashboards and metrics reporting for harm verticals. They will conduct analyses and generate insights to inform improvements to review, detection, or enforcement, and influence safety roadmaps. The role involves optimizing LLM prompts for measurement purposes, collaborating with other safety teams to understand key safety concerns and create relevant policies, providing metrics for leadership and external reporting, and developing automation to scale their work using agentic products. The position may involve resolving urgent escalations outside normal work hours and may require working with sensitive content including sexual, violent, or otherwise disturbing material.
Head of Product Data Science
As the leader of the data science function at Abridge, the responsibilities include building and managing a world-class data science team, growing it to 12+ members over twelve months, and fostering a high-impact collaborative team culture. The role involves driving product strategy through data-driven insights, including user behavior analysis, product performance metrics, and causal inference experimentation. It requires partnering with product, strategy, and research teams to develop ROI frameworks for customers using real-time data. Collaboration with the research team is key on models, model evaluation frameworks, production performance monitoring, and defining clinically relevant quality metrics. The leader must effectively communicate the data strategy, complex analyses, and key insights to cross-functional partners including executives. Structuring the company's data strategy in close partnership with the Data Engineering team includes identifying data source gaps, ingesting internal and external data, and optimizing data structures for use. This role also includes making critical technical infrastructure decisions such as tooling choices, build vs buy trade-offs, and setting technical standards to enable fast team progress without incurring technical debt. Furthermore, the leader is responsible for building a future-oriented data science organization that incorporates best practices and AI to accelerate data ingestion and insight generation.
Enterprise Account Executive KSA
Help users discover and master the Dataiku platform through user training, office hours, demos, and ongoing consultative support. Analyse and investigate various kinds of data and machine learning applications across industries and use cases. Provide strategic input to the customer and account teams that help our customers achieve success. Scope and co-develop production-level data science projects with our customers. Mentor and help educate data scientists and other customer team members to aid in career development and growth.
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