Solutions Architect

At Fiddler, we understand the implications of AI and the impact that it has on human lives. Our company was born with the mission of building trust into AI. The rise of Generative AI and Agents has unlocked generalized intelligence but also widened the risk aperture and made it harder to ensure that AI applications are working well. Fiddler enables organizations to get ahead of these issues by helping deploy trustworthy, and transparent AI solutions.

Fiddler partners with AI-first organizations to help build a long-term framework for responsible AI practices, which, in turn, builds trust with their user base. AI Engineers, Data Science, and business teams use Fiddler AI to monitor, evaluate, secure, analyze, and improve their AI solutions to drive better outcomes. Our platform enables engineering teams and business stakeholders alike to understand the “what”, “why”, and “how” behind AI outcomes.

Our Founders

Fiddler AI is founded by Krishna Gade (engineering leader at Facebook, Pinterest, Twitter, and Microsoft) and Amit Paka (product leader at Microsoft, Samsung, Paypal and two-time founder). We are backed by Insight Partners, Lightspeed Venture Partners, and Lux Capital.

Why Join Us

Our team is motivated to help build trust into AI to enable society harness the power of AI. Joining us means you get to make an impact by ensuring that AI applications at production scale across industries have operational transparency and security. We are an early-stage startup and have a rapidly growing team of intelligent and empathetic doers, thinkers, creators, builders, and everyone in between. The AI and ML industry has a rapid pace of innovation and the learning opportunities here are monumental. This is your chance to be a trailblazer.

Fiddler is recognized as a pioneer in the field of AI Observability and has received numerous accolades, including: 2022 a16z Data50 list, 2021 CB Insights AI 100 most promising startups, 2020 WEF Technology Pioneer, 2020 Forbes AI 50 most promising startups of 2020, and a 2019 Gartner Cool Vendor in Enterprise AI Governance and Ethical Response. By joining our brilliant (at least we think so) team, you will help pave the way in the AI Observability space.

The Mission:

As a Solutions Architect, Customer Success at Fiddler, your mission is to ensure our customers achieve meaningful, measurable outcomes from their AI observability investments. You serve as both a technical expert and trusted advisor, bridging the gap between complex ML/LLM systems and real-world business value. By guiding customers through onboarding, building seamless integrations, and championing their needs across the organization, you help them operationalize trustworthy AI at scale while fueling Fiddler’s growth through successful adoption, renewals, and expansion. About The Team:

You’ll join a tight-knit, highly collaborative Customer Success Engineering team that partners closely with some of the world’s leading enterprise AI organizations. We operate remotely but stay deeply connected through constant communication, collaboration, and shared purpose. Our team thrives on knowledge sharing, peer learning, and collective problem solving; no one works in a silo.

We celebrate each other’s successes, support one another through complex challenges, and take pride in helping our customers achieve real-world impact with Fiddler’s AI Observability platform. Every project is a team effort, and every win is shared. If you love working alongside smart, driven peers who genuinely care about both customer success and each other’s growth, you’ll feel right at home here. What You’ll Do:

• 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.

• Bachelor’s degree in Computer Science (AI/ML focus), Statistics, Mathematics, or related field with 5-7+ years of professional experience.
• 2+ years of hands-on experience deploying, monitoring, or maintaining ML models in production environments.
• Excellent communication, presentation, and storytelling abilities; able to distill complex technical concepts into clear, actionable insights for both technical and executive audiences.
• Strong organizational and project management skills, with the ability to balance multiple customer engagements and priorities.
• Demonstrated collaboration across cross-functional teams-Product, Engineering, and Delivery-to drive customer outcomes.
• A customer-first mindset with empathy, curiosity, and a deep sense of ownership for delivering value.
• Passion for continuous learning and a desire to inspire customers and peers through thought leadership and technical credibility.

Even Better

• Understanding of data science concepts, model interpretability, and explainability techniques used in modern ML systems.
• Working knowledge of data and workflow tools such as Hadoop, MongoDB, Snowflake, BigQuery, Spark, Kafka, Kinesis, RabbitMQ, Airflow, MLflow, Luigi, Kubeflow, or Argo.
• Experience with machine learning frameworks like TensorFlow, PyTorch, or Scikit-learn.
• Proficiency with Kubernetes and cloud platforms (AWS, Azure, GCP).
• Familiarity with the ML/DS lifecycle, including feature generation, model training, deployment, monitoring, and evaluation (batch and real-time scoring via REST APIs).
• Understanding of emerging AI technologies-Generative AI, Large Language Models (LLMs), RAG architectures, and agent-based systems. For candidates in the San Francisco Bay Area, this role is a hybrid position requiring working from our Palo Alto office 3 days a week.

Be an Early Applicant Remote Hiring Remotely in US 160K-230K Annually Senior level Remote Hiring Remotely in US 160K-230K Annually Senior level Guide enterprise customers through onboarding, integrations, adoption, and expansion of an AI observability platform. Architect deployment solutions, write custom integrations, advise data science and ML engineering teams, lead technical engagements and escalations, communicate customer needs internally, and promote model transparency, monitoring, governance, and measurable business outcomes. The summary above was generated by AI, US locations $160,000-$200,000 + equity San Francisco, New York City & Seattle $190,000-$230,000 + equity

Benefits & Perks

• Competitive pay + equity
• Unlimited PTO
• Premium health, dental & vision (100% premium coverage for employees)
• 401(k) plan
• Monthly fitness reimbursement
• Paid parental leave

Palo Alto HQ Vibes

• Annual Caltrain pass
• Monthly in-office massages
• Fastrak reimbursement
• Lunch provided Mon-Thurs

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