Prove AI

We are looking for a Principal Engineer who will sit at the technical center of our AI platform—owning architecture, standards, and production excellence across the stack. This is not a pure research role, nor a model R&D position. You will lead the productionization of GenAI systems—owning evaluation pipelines, tracing and observability, inference orchestration, safety guardrails, dataset/prompt workflows, and developer-facing tooling. You will translate ambiguous product problems into reliable, scalable platform capabilities that customers trust with production workloads. You will operate as a force multiplier—setting architectural standards, mentoring senior engineers, influencing roadmap trade-offs, and partnering directly with product and executive leadership to shape technical direction. Required Experience - 10+ years of software engineering experience with recent hands-on coding - Experience operating at Principal / Distinguished / Staff / Lead Engineer level (or equivalent while still coding) - Proven ownership of production GenAI systems (RAG, eval frameworks, inference services, safety/guardrails, tracing/observability) - Deep expertise in Python - Strong practical capability in TypeScript, React, and Node.js - Strong cloud and platform experience (AWS/GCP/Azure, Kubernetes, CI/CD, distributed systems) - Experience driving multi-team architecture decisions - Ability to communicate trade-offs clearly to senior leadership Strong Plus - RAG evaluation frameworks (ragas, custom eval harnesses) - Vector databases (Pinecone, Weaviate, Milvus, Qdrant) - OpenTelemetry instrumentation - Performance optimization of inference systems - Exposure to blockchain/tamper-evidence concepts - Own technical vision and architecture across GenAI platform capabilities - Lead end-to-end productionization of AI features (not prototypes) - Define and operate readiness criteria for AI releases (eval gates, rollout strategy, rollback mechanisms, SLOs) - Design and own core platform capabilities: - Evaluation pipelines - Tracing and observability systems - Prompt/version/dataset workflows - Guardrails and safety systems - Inference orchestration layers - Establish and track metrics for latency (p95/p99), cost efficiency, quality, and safety - Write clear architectural proposals that influence multi-team decisions - Partner with Product to translate ambiguous user pain into staged technical delivery - Raise the engineering bar through reviews, documentation, mentoring, and principled system design - Contribute hands-on across Python services, Node/TypeScript APIs, and React UI when necessary - Join at the ground floor of a GenAI infrastructure company defining a new category - Shape the technical foundation before the architecture calcifies - Work directly with senior product and engineering leadership - Build production systems that AI teams depend on daily - Fully remote - ESOP equity - Flexible hours - Generous PTO - Global offsites - Education support - Clear advancement path

We are looking for a Principal Engineer – AI/ML Platform who will own the architecture, productionization, and operational excellence of our machine learning and LLM infrastructure.This is not a research scientist role. You will define how GenAI systems are evaluated, deployed, monitored, governed, and continuously improved at scale. You will shape standards across model integration, evaluation frameworks, inference systems, safety mechanisms, telemetry instrumentation, and AI/ML workflow automation. You will operate at the intersection of AI engineering, distributed systems, and platform architecture—partnering closely with Product and Engineering leadership to ensure our AI systems are reliable, observable, safe, and economically scalable in enterprise production environments. Required Experience - 10+ years of software engineering experience with significant recent hands-on AI/ML work - Proven ownership of production AI/ML or LLM systems at scale (not research or prototypes) - Deep expertise in LLM productionization (RAG, finetuning, evaluation, guardrails, model monitoring) - Strong Python expertise - Experience with modern AI frameworks (PyTorch, TensorFlow, JAX, Scikit-learn) - Hands-on AI/MLOps experience (CI/CD for ML, deployment automation, experiment tracking, monitoring) - Experience with cloud platforms (AWS/GCP/Azure), Kubernetes, and distributed systems - Experience implementing evaluation pipelines and observability instrumentation - Demonstrated technical leadership influencing multi-team architectural direction Strong Plus - Experience with ML workflow orchestration platforms (Kubeflow, MLflow, Vertex AI, SageMaker) - Expertise in model governance, bias evaluation, compliance, and drift detection - Domain expertise in NLP, agentic systems, recommender systems, or similar applied AI areas - Open-source AI/ML contributions - Master’s or PhD in ML/AI-related field - Define and own architecture for scalable AI/ML and LLM systems, including: - Inference pipelines - Evaluation frameworks - Model lifecycle workflows - Monitoring and observability systems - Translate ambiguous business requirements into robust AI platform designs and staged delivery plans - Make strategic decisions on: - Model integrations and gateways - Retrieval-augmented generation (RAG) approaches - Evaluation methodologies - Safety and guardrail systems - Establish standards for model readiness, evaluation gates, rollout/rollback mechanisms, and drift detection - Build and deploy production-grade LLM capabilities integrated into distributed systems with clear SLOs and telemetry - Design scalable AI/MLOps and AIOps practices across training, testing, deployment, and monitoring - Improve data pipelines, feature workflows, and lineage processes supporting model evaluation and inference - Instrument tracing and model observability using OpenTelemetry and modern telemetry standards - Own evaluation pipelines tracking latency, cost, accuracy, hallucination rates, and prompt/version drift - Provide clear trade-off analyses balancing model performance, cost efficiency, safety, and maintainability - Write structured technical proposals that guide executive investment and roadmap decisions - Mentor engineers in AI productionization, experimentation discipline, and distributed systems design - Raise the engineering bar through principled reviews, documentation, and mechanism-driven standards - Shape the AI production architecture of a category-defining GenAI infrastructure company - Define how enterprise-grade AI systems are observed, evaluated, and remediated - Build mechanisms that scale beyond individual engineers - Influence roadmap and platform strategy at a formative stage - Fully remote - ESOP equity - Flexible hours - Generous PTO - Global offsites - Education support - Clear advancement opportunities

We are looking for a Senior Software Engineer who can take end-to-end ownership of complex full-stack systems powering our GenAI platform. This role is focused on experienced, hands-on engineers who thrive in ambiguity and can translate product intent into reliable production systems. You will build and ship production-grade AI capabilities—spanning evaluation pipelines, tracing systems, platform APIs, and developer-facing UI. You will work closely with product and engineering leadership to deliver real AI infrastructure used by customers in production—not demos or experimental tooling. Required Experience - 5–10+ years of professional engineering experience - Strong hands-on experience building production systems - Experience building or integrating LLM-powered systems (RAG, embeddings, workflows, agents) - Strong TypeScript, React, and Node.js capability - Practical understanding of AI production concerns (evaluation, safety, latency, cost trade-offs) - Ability to ship full-stack features without prolonged ramp-up - Strong debugging and performance optimization skills Strong Plus - Experience with evaluation frameworks or safety tooling - Familiarity with OpenTelemetry and distributed tracing - Exposure to vector databases and AI data pipelines - Experience with Python-based AI services - Design and ship production-grade AI platform features - Build across the stack using TypeScript, React, Node.js, and Python where required - Implement and improve observability, telemetry, and AI tracing systems - Contribute to evaluation pipelines and quality measurement systems - Take ownership of features from shaping → implementation → release → iteration - Improve system reliability, performance, and cost efficiency - Contribute to architectural discussions and engineering standards - Operate independently while collaborating closely with product and peers - Work at the intersection of GenAI, infrastructure, and observability - Ship real AI systems used by production engineering teams - Join at a stage where your impact materially shapes the product - Operate in a fast-moving, high-ownership environment - Fully remote - ESOP equity - Flexible hours - Generous PTO - Global offsites - Education support - Clear advancement opportunity

We are looking for a Product Marketing Lead who will report to our CMO and will sit at the intersection of product, engineering, and marketing—collaborating on the end-to-end user journey; turning user interaction and feedback into clear insight to inform positioning, messaging, and adoption. This is not a traditional launch-only PMM role. You will work closely with product and engineering during a formative phase of development, helping us understand how GenAI builders actually troubleshoot, what signals they care about, and how value is realized from first interaction to sustained usage. You will translate that insight into differentiated messaging, developer-native experiences, and focused growth experiments. You will play a critical role in shaping ProveAI’s wedge into the GenAI observability market and scaling adoption as we move toward our next product phase. Required Experience - 5+ years in product marketing, developer marketing, or a closely related role within SaaS, dev tools, infrastructure, or AI platforms. - Demonstrated experience owning user journeys and product insight, not just launches or campaigns. - Proven ability to work deeply with product and engineering teams in technical environments. - Experience positioning technical products for builders, makers, or AI/ML practitioners. Strong Plus - Exposure to GenAI, ML platforms, observability, infrastructure, or developer tooling. - Comfort working with open-source products and communities. - Data-driven mindset with the ability to blend analytics, research, and intuition. - Experience operating in early-stage or fast-scaling environments. - Own and continuously optimize the end-to-end user journey—from first discovery through activation, adoption, and long-term retention—across both open-source and commercial products. - Identify friction, drop-offs, and moments of value, ensuring the experience feels native to developer and AI engineer workflows rather than traditional enterprise funnels. - Partner closely with Product and Engineering to gather structured user insight (interviews, direct conversations, communities, surveys, and usage data) and translate it into clear product, messaging, and prioritization recommendations. - Act as a strategic insight partner to the Head of Product, clarifying what users value, which capabilities resonate, and why. - Define and evolve clear, credible positioning and value propositions for GenAI builders, articulating Prove AI’s differentiation in observability, telemetry ownership, remediation, and production reliability. - Lead positioning and messaging for product releases and major milestones, ensuring launches are grounded in real user needs and measured by adoption and usage—not just announcements. - Collaborate with content, growth, and community teams to guide users through discovery, evaluation, and adoption, using data-driven experimentation to refine messaging and engagement. - Join at the ground floor of a GenAI infrastructure company defining a new category within observability and remediation. - Shape positioning, messaging, and user adoption while the product and market are still being formed. - Work directly with senior product and engineering leaders to influence roadmap and strategy. - Build for the future—without legacy constraints—using open standards and modern GenAI workflows. - Help create a product that engineers trust with their most critical AI systems. - Fully remote, work-from-anywhere setup – design your day and do your best work from home or wherever you’re most productive. - Meaningful equity via our Employee Share Option Plan (ESOP) – you’ll own a real stake in what we’re building and share in the long-term upside. - Flexible working hours – we care about outcomes, not clock-watching. Balance work with life on your terms. - Generous paid time off – take the time you need to recharge and avoid burnout. - Global in-person offsites (travel permitting) – connect, collaborate, and build relationships with the team in inspiring locations around the world. - Continued education & learning support – we actively invest in your growth through courses, conferences, and professional development. - Clear advancement opportunities – join at a stage where your impact is visible, your voice matters, and your progression is real—not capped by layers of hierarchy.