Note: The job is a remote job and is open to candidates in USA. mpathic is building infrastructure for trustworthy, safe, aligned, and emotionally intelligent AI systems. The Technical Product Manager will own platform features from idea through launch, translating customer and domain needs into requirements, coordinating engineering and design, and establishing quality, evaluation, and annotation standards.
Responsibilities
- Be the single owner for the platform features you shepherd, carrying each from intake through scope, build, demo, and launch
- Decide, with the CTO and Head of AI Product, what is ready to build, and keep the plan and its rationale clear for everyone involved
- Run the demo and feedback cadence, capture feedback in Linear, and keep scope honest as the work evolves
- Turn validated direction into precise product requirements, with explicit scope, non-goals, and one clear success metric per feature
- Partner on design (Figma where there is UI) and write acceptance criteria engineering can build against
- Document the decisions that hold a feature together, so nothing important lives only in someone's memory
- Define what “good” means for the features you own, and the rubrics and evaluations that measure it
- Set annotation and human-data quality standards that are consistent, auditable, and humane for the people doing the work
- Talk to customers and domain experts, and bring the front-line delivery teams (Post Sales Support and AI Labs Services Delivery) into planning on purpose
- Turn what you learn into roadmap candidates rather than one-off workarounds
- Keep engineering, the Head of AI Product, research, sales, and delivery aligned on what is being built and why
- Translate between technical, clinical, and commercial perspectives so the right tradeoffs get made
- Build a deep understanding of the platform, its users, and the high-stakes domains it serves, and a clear picture of how work flows from idea to launch today
- Take ownership of one or two in-flight features, write or sharpen their requirements, and shepherd them through build and demo to launch
- Establish a lightweight, shared way of writing requirements and capturing decisions in Linear, so the team has one source of truth
- Build trusted relationships across engineering, the Head of AI Product, research, sales, and the delivery teams
- Own the end-to-end delivery of platform features on the full path, from requirements through launch and follow-up, in partnership with CTO and Head of AI Product
- Turn validated direction from the Head of AI Product and research into precise PRDs, designs (with Figma where there is UI in partnership with design), and clear acceptance criteria
- Stand up the rubrics, evaluation, and annotation-quality standards that make platform outputs trustworthy and auditable for internal teams and customers
- Build a dependable feedback loop with the front-line delivery teams (Post Sales Support and AI Labs Services Delivery) so their needs reach the roadmap
- Review recordings, collaborate with GTM org for discovery and translation of technical product capabilities throughout the pre-sales and post-sales process
- Help define how product management scales at mpathic as the platform and team grow
Skills
- * 7+ years as a technical product manager, technical program manager, or product-minded engineer building software, data, or AI platforms, owning features from requirements through production
- * Technical depth to be a credible peer to engineers: you can read and reason about code, system design, and data models well enough to weigh tradeoffs, even if you do not write production code day to day
- * A track record of defining a product's technical contracts: APIs, data schemas, evaluation metrics, and acceptance criteria that engineering can build against
- * Shipped at least one of: an evaluation or benchmarking system, red-team or safety-testing tooling, an annotation or labeling pipeline, or a dataset or data-processing platform
- * Working fluently with the modern AI application stack (LLM orchestration, retrieval, evaluation pipelines, and monitoring) and the delivery toolchain (version control, feature flags, CI, and an issue tracker such as Linear)
- * Experience in high-stakes or regulated domains such as Trust & Safety, where auditability and rigor are required
- * Experience turning taxonomies and rubrics into measurable, repeatable evaluation workflows in software
- * Familiarity with reinforcement-learning environments, reward modeling, or agent evaluation harnesses
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