Senior Product Manager – Data & Quality
Partner with frontier AI research labs to design datasets and environments that improve model performance. Lead technical conversations with customer researchers to understand model capabilities, failure modes, data requirements, and success criteria. Probe model behavior through systematic evaluation to uncover weaknesses and identify high-impact data interventions. Design evaluation frameworks, calibration processes, and quality rubrics that establish measurable project success metrics. Develop technical specifications for data projects that balance research rigor with operational feasibility. Serve as thought partner to customer research teams throughout the sales cycle, building trust and credibility. Stay current on frontier AI research, RL environment design, post-training techniques, and evaluation methodologies.
Head of Product, AI
Own the end-to-end AI product strategy grounded in technical feasibility and real-world constraints, translate model capabilities, data limitations, and evaluation results into clear product decisions, make trade-offs across quality, latency, cost, reliability, and user experience, work daily with ML, backend, and mobile engineers on design, evaluation, and iteration, define success metrics and feedback loops across offline evaluation, online experiments, and human feedback, drive execution with clear specifications, risk awareness, and disciplined prioritization, ensure AI features ship quickly, safely, and reliably into production, and own AI product quality across UX, correctness, and outcomes.
Head of Product, AI
Own the end-to-end AI product strategy grounded in technical feasibility and real-world constraints; translate model capabilities, data limitations, and evaluation results into clear product decisions; make hard trade-offs across quality, latency, cost, reliability, and user experience; work daily with ML, backend, and mobile engineers on design, evaluation, and iteration; define success metrics and feedback loops across offline evaluation, online experiments, and human feedback; drive execution with clear specifications, risk awareness, and disciplined prioritization; ensure AI features ship quickly, safely, and reliably into production; and own AI product quality across user experience, correctness, and outcomes.
Product Manager, Models
As the Product Manager for Heidi's models platform, you will own the product strategy and roadmap for the platform including evaluation pipelines, fine-tuning infrastructure, model routing, and safety systems. Your responsibilities include prioritising your team's work across enablement requests, model safety and quality, and new capability bets; fixing platform issues that cause blocks for product teams; building evaluation tooling and fine-tuning workflows usable in clinical settings; deciding improvements based on clinician feedback, model quality signals, and product team needs; allocating engineering capacity among competing requests and clearly communicating deferrals; working with engineers on evaluation design, fine-tuning trade-offs, and model architecture decisions; setting model quality and safety targets based on clinical outcomes; consolidating duplicate infrastructure across product teams; and monitoring foundation model developments to adjust the roadmap accordingly. You will collaborate closely with engineers, researchers, product PMs, and clinical safety teams and report to product leadership. This is a platform role whose outputs impact every user-facing product at Heidi.
Research Product Manager — Structured AI Systems
The Research Product Manager is responsible for advancing foundational work in tabular data learning, structured and relational representation learning, compression-aware AI, hybrid symbolic, relational, and neural systems, and large-scale systems, linking these research efforts to real production systems managing petabytes of data. The role involves productionizing structured AI models by collaborating with Research and Systems teams to design training on Parquet/Iceberg/Delta data, define training infrastructure requirements, inference architectures, and maintenance loops, while understanding storage and compute trade-offs, data layout, compute scheduling, model lifecycle, infrastructure bottlenecks, and evaluation pipelines. The role also involves defining economic value extraction by identifying buyers, economic value sources, quantification methods, and converting research advances into revenue and platform advantages, requiring strong enterprise infrastructure economic intuition. Additionally, the Research Product Manager identifies viable modeling advances for production, terminates non-viable research directions, defines integration paths into enterprise workloads, and works with the Chief Research Scientist on research agenda prioritization. The position requires deep understanding of large AI model training, deployment, and maintenance in production systems, as well as translating foundational modeling advances into economically valuable infrastructure, shaping technical execution and economic strategy.
Product Manager, AI Platform
Own the product strategy and roadmap for managed inference services, including model deployment, autoscaling, multi-LoRA serving, and inference optimization; define requirements for agent platform capabilities such as structured outputs, function calling, memory primitives, tool integration, and multi-step reasoning workflows; drive decisions on inference optimizations like speculative decoding, continuous batching, KV cache management, quantization support, and custom kernel integration; partner with ML infrastructure engineers to design APIs, SDKs, and deployment workflows that support model fine-tuning, version management, and A/B testing; work with datacenter teams to optimize GPU allocation strategies balancing dedicated versus serverless deployments, cold start latency, and cost-per-token economics; analyze competitive offerings from inference-first competitors; define pricing models aligned with customer usage patterns while maintaining healthy unit economics; conduct customer research to understand inference workload requirements; translate customer feedback into feature specifications including support for new model architectures, framework integrations, and observability tooling; and build go-to-market materials such as reference architectures, performance benchmarks, cost calculators, and migration guides for customers moving from self-hosted or competing platforms.
Staff Product Manager, AI-Powered Workflows
Define and own the product vision, strategy, and roadmap for Vanta's AI-centric workflow builder. Conduct rigorous AI evaluations and performance assessments, continuously analyzing data to optimize AI-powered features. Partner deeply with Engineering and AI teams to design the technical architecture for workflow orchestration at scale. Partner closely with teams across Vanta to understand all potential use cases and distill a clear direction for impact. Drive product discovery with upmarket customers to understand their custom compliance workflow needs and translate them into product requirements. Lead cross-functional execution to deliver the workflow builder, managing dependencies across multiple teams and ensuring timely delivery of this strategic initiative. Establish measurement frameworks and success metrics to track product adoption, AI performance, and customer value.
Senior Product Manager – Platform
Partner with customers to build and deploy impactful Gen AI and machine learning solutions, from use case scoping and data exploration to model development and deployment, leveraging Snorkel Flow or designing custom approaches. Develop and implement state-of-the-art AI systems such as retrieval-augmented generation (RAG), fine-tuning pipelines, prompt engineering recipes, and agentic workflows. Create augmented real-world datasets and comprehensive evaluation workflows to ensure model reliability, transparency, and stakeholder trust. Forge and manage relationships with customers’ leadership and stakeholders to ensure successful AI project development and deployment with Snorkel Flow. Collaborate closely with pre-sales Solutions and Product teams to align customer needs with capabilities, prioritize roadmap gaps, and guide successful project setups. Work with other Applied AI Engineers to standardize solutions and contribute to internal tooling and best practices. Lead stakeholder education on quantitative capabilities and AI approach strengths and weaknesses. Serve as the voice of customers for new AI paradigms and workflows, sharing feedback with product teams. Conduct enablement workshops to transfer knowledge to customers using or considering Snorkel AI. The role includes up to 25% annual travel.
AI Product Manager
As an AI Product Manager at Air Apps, you will define and drive the AI product roadmap to align with business objectives and user needs. You will collaborate with cross-functional teams such as engineering, design, and marketing to develop and launch AI-powered features. Your role includes conducting market research and analyzing user feedback to identify AI integration opportunities, working closely with data scientists and machine learning engineers to optimize AI models for accuracy, performance, and user impact, defining key performance indicators (KPIs) to measure success and iterating based on data-driven insights, staying up to date with AI trends and emerging technologies to keep products competitive, and ensuring ethical AI usage and compliance with data privacy regulations.
Product Manager, Agent Memory
Lead development of Agent Memory, the system that enables AI agents to remember and personalize interactions across conversations, transforming one-off exchanges into continuous relationships that drive measurable business outcomes at scale. Balance competing stakeholder needs including end users expecting personalization, operations teams requiring compliance, and developers needing flexible integration patterns. Define how AI agents should remember, addressing session continuity, long-term relationships, intelligent consolidation, context retrieval, and privacy-preserving personalization. Collaborate closely with engineering teams on distributed systems, ML teams on retrieval and embedding technologies, and infrastructure teams on scalable storage solutions. Serve as a trusted and strategic advisor to customers in partnership with sales, go-to-market, and forward-deployed teams. Lead the product through all stages from concept to execution in collaboration with cross-functional partners.
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