Fiddler AI

Solutions Engineer

• Drive successful onboarding experiences. Partner closely with a Fiddler Delivery Manager to architect and implement onboarding solutions, aligning customer goals with Fiddler’s capabilities, and ensuring deployments are delivered on time, with precision, and with clear success criteria met.
• Be the trusted technical partner our customers rely on. You’ll build strong relationships with data science and ML engineering teams, guiding them through every stage of their AI observability journey and ensuring they realize measurable value from Fiddler.
• Champion the customer voice across Fiddler. Lead ongoing technical engagements, status syncs, roadmap discussions, QBRs, and escalation management; to ensure customer feedback influences our product roadmap and long-term strategy.
• Become a domain expert in AI Observability. Master Fiddler’s platform and help customers operationalize observability best practices improving model transparency, performance monitoring, and compliance across their ML lifecycle.
• Deliver seamless integrations and technical success. Write custom integration code that connects Fiddler to customer data ecosystems using tools like Snowflake, Airflow, MLflow, S3, Kafka, and more; ensuring robust, scalable, and secure pipelines.
• Accelerate platform adoption. Build and refine integration patterns between Fiddler and common data platforms, workflow tools, and ML infrastructures to reduce time-to-value for new customers.
• Uncover and drive expansion opportunities. Identify new ways Fiddler can provide impact; whether through advanced observability use cases, expanded integrations, or deeper model governance, helping drive renewals and growth.

Solutions Architect, Customer Success

Developer Relations

• Educate. Be an industry thought leader and evangelist for the Fiddler AI Control Plane
• Speak. Represent Fiddler at conferences, industry events, and meetups
• Create. Build thought-provoking, viral, and fun content and conversations across podcasts, videos, blogs, whitepapers, social threads, GitHub repos, tutorials, interactive tools, lightweight apps, and prototypes
• Nurture. Build relationships with, be present and be engaging in online communities
• Connect. Speak directly with our users about their pain points and needs. Give demos as needed.
• Influence. Advocate for the needs of our community and bring their feedback back to the product and marketing teams
• Extend. Identify partnership opportunities with complementary projects and companies in the agentic AI ecosystem to broaden Fiddler's reach

Manager

• Build and scale account-based programs that drive engagement, pipeline, and revenue.
• Lead ABM strategy and execution across priority accounts, segments, industries, personas, and pipeline goals.
• Build integrated campaigns across email, paid media, webinars, events, content, digital, SDR activation, executive engagement, and sales plays.
• Develop account journeys, campaign themes, content plans, messaging, landing page concepts, sales enablement, and follow-up motions.
• Use AI to improve account research, personalization, content repurposing, messaging tests, campaign QA, reporting, and performance analysis.
• Use data, intent signals, enrichment, engagement insights, and funnel reporting to improve targeting, orchestration, and conversion.
• Continuously test and optimize messaging, audiences, channels, offers, content formats, and sales follow-up.

Senior Manager, ABM & Campaigns

Product Designer

• Develop design patterns and principles that help users understand AI behavior, surface insights, and make confident decisions
• Design end-to-end product experiences across Fiddler's platform; Partner with engineers, product managers, and data scientists to bring Fiddler to life through experiences that are intuitive and built for enterprise scale
• Conduct user research and discovery to understand how technical teams evaluate, monitor, and govern AI in production
• Maintain and evolve the design system, authoring reusable components and interaction patterns that scale across the product
• Translate technically complex capabilities into world class experiences, keeping customers and user experience at the forefront

Senior Product Designer

AI Research Scientist

• Lead applied research and development for the models and datasets at the core of Fiddler's Trust Service and suite of guardrail classifiers and evaluators that customers depend on to keep their LLM and agentic applications safe, accurate, and compliant in production.
• Partner closely with other engineering teams, Product, and Customer Success to build strong relationships with customer data science and ML engineering teams.
• Design, train, and ship production classifiers for safety, security, and quality detection under strict latency and cost constraints.
• Lead the development of synthetic and adversarial dataset pipelines, including novel methods for generating, filtering, and validating data that exposes failure modes our models need to learn.
• Drive the technical direction of generative insights – the LLM- and agent-powered analysis layer that helps customers diagnose what's going wrong in their AI applications and why.
• Contribute to the evaluation and experimentation infrastructure that lets the AI Science team and our customers reliably measure model quality, regression, and drift across rapidly evolving model populations.
• Explore reinforcement learning and preference-based methods where they offer real leverage over supervised baselines.
• Collaborate with Backend and Platform engineers to take research prototypes from notebook to a hardened, scaled, observable service.
• Partner with Product, Solutions Engineering, and Customer Success to translate enterprise customer needs into research problems and translate research results back into product.
• Mentor AI Scientists on the team, raise the technical bar through code review and design review, and represent Fiddler externally through publications, talks, or open-source contributions when appropriate.

Staff AI Scientist

Platform Engineer

• Set technical direction for the data platform — Own the architecture roadmap for Fiddler's ingestion, storage, and query layers. Drive multi-quarter initiatives from problem framing through design, implementation, and rollout.
• Design systems for 10x scale — Lead the evolution of our ClickHouse-backed analytics layer and Kafka-based ingestion pipeline to handle order-of-magnitude growth in event volume, query complexity, and tenant count.
• Define the event model for next-generation AI workloads — Architect the data model and storage strategy for agentic application traces, LLM evaluation pipelines, and enrichment workflows — balancing flexibility, query performance, and schema evolution.
• Drive cross-team technical decisions — Partner with the Backend, Monitoring, and Enrichment teams to ensure platform abstractions serve their needs. Represent the Platform perspective in company-wide architecture reviews.
• Own platform reliability and cost efficiency — Establish SLOs, capacity planning processes, and cost optimization strategies for data infrastructure. Make build-vs-buy decisions for infrastructure components.
• Raise the engineering bar — Mentor senior engineers. Establish patterns and guardrails (data modeling conventions, query optimization practices, testing strategies) that compound across the team. Lead by example in code review, design docs, and incident response.
• Influence product direction — Work with Product and Customer Engineering to translate customer data challenges into platform capabilities. Help define what's feasible, what's risky, and what to build next.

Staff Platform Engineer – IND

• Deliver a production-ready auth layer for GenAI workloads — Ship OAuth, machine-to-machine (M2M) authorization, and an LLM/MCP authorization layer that secures how enterprises connect agentic applications to Fiddler at scale.
• Launch GenAI-specific alerting capabilities — Deliver new alert types that detect quality, safety, and performance issues in LLM applications, with notification integrations (PagerDuty, webhooks) and the APIs to configure them.
• Ship backend features end-to-end — Own features from API design through async processing (Kafka, Celery) to production rollout across Fiddler's microservices — including monitoring, ingestion, and LLM evaluation workflows.
• Keep services reliable and observable — Maintain SLOs, instrument with Prometheus/OpenTelemetry, and participate in on-call. When things break, you fix them.