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About This Role
Our Purpose
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:
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As a Senior AI Solutions Engineer 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 Agentic AI 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:
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You’ll join a tight\-knit, highly collaborative AI Solutions 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 and Control Plane 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:
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- Drive successful Proof\-of\-Value experiences. Partner closely with a Fiddler go\-to\-market teams to architect and implement AI solutions, aligning prospect goals with Fiddler’s capabilities, and ensuring evaluation cycles 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 AI 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 AI lifecycle.
- Deliver seamless integrations and technical success. Write custom integration code that connects Fiddler to customer data ecosystems using tools such as 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 AI 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.
What We’re Looking For
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- 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 AI 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 Sales—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
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- West Coast PST time zone
- Understanding of data science concepts, model interpretability, and explainability techniques used in modern ML systems.
- History of direct support of government or governmental agencies/programs
- Experience building GenAI Applications and Agentic workflow applications.
- 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.
- Familiarity with Federal accounts and sales motions
*For candidates in the San Francisco Bay Area, this role is a hybrid position requiring working from our Palo Alto office 2\-3 days a week.*
Compensation:
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US locations $164,000 \- $215,000K OTE \+ benefits \& equity
Benefits \& Perks
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- Competitive pay \+ equity
- Premium health, dental \& vision (*100% premium coverage for employees)*
- Open PTO
- 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
The posted range represents the expected salary range for this job requisition and does not include any other potential components of the compensation package and perks previously outlined. Ultimately, in determining pay, we'll consider your experience, leveling, location, and other job\-related factors.
Fiddler is proud to be an equal opportunity employer. We celebrate diversity and are committed to creating an inclusive environment for all employees. If you require special accommodations in order to complete the interviews or perform job duties, please inform the recruiter at the beginning of the process.
Beware of job scam fraud. Our recruiters use @fiddler.ai email addresses exclusively. In the US, we do not conduct interviews via text or instant message, or ask for sensitive personal information such as bank account or social security numbers.
Role Details
About This Role
AI/ML Engineers build and deploy machine learning models in production. They work across the full ML lifecycle: data pipelines, model training, evaluation, and serving infrastructure. The role has evolved significantly over the past two years. Where ML Engineers once spent most of their time on model architecture, the job now tilts heavily toward inference optimization, cost management, and integrating LLM capabilities into existing systems. Companies want engineers who can ship production systems, and the experimenter-only role is fading fast.
Day-to-day, you're writing training pipelines, debugging data quality issues, setting up evaluation frameworks, and figuring out why your model performs differently in staging than it did on your dev set. The best ML engineers are obsessive about reproducibility and measurement. They instrument everything. They know that a model is only as good as the data feeding it and the infrastructure serving it.
Across the 3,708 AI roles we're tracking, AI/ML Engineer positions make up 70% of the market. At Fiddler AI, this role fits into their broader AI and engineering organization.
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
What the Work Looks Like
A typical week might include: debugging a data pipeline that's silently dropping 3% of training examples, running A/B tests on a new model version, writing documentation for a feature flag system that lets you roll back model deployments, and reviewing a junior engineer's PR for a new evaluation metric. Meetings tend to be cross-functional since ML touches product, engineering, and data teams.
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
Skills Required
Python and PyTorch dominate the requirements. Most roles expect experience with cloud platforms (AWS, GCP, or Azure) and familiarity with ML frameworks like TensorFlow or JAX. RAG (Retrieval-Augmented Generation) has become a top-3 skill requirement as companies integrate LLMs into their products. Docker and Kubernetes show up in about a third of postings, reflecting the production focus of the role.
Beyond the core stack, employers increasingly want experience with experiment tracking tools (MLflow, Weights & Biases), feature stores, and vector databases. Fine-tuning experience is valuable but less common than you'd think from reading Twitter. Most production LLM work is RAG and prompt engineering, not fine-tuning. If you have both, you're in a strong position.
Companies that are serious about AI/ML hiring tend to post specific infrastructure details in the job description: the frameworks they use, their model serving stack, their data pipeline tools. Vague postings that just say 'ML experience required' without specifics are often companies that haven't figured out what they need yet.
Compensation Benchmarks
AI/ML Engineer roles pay a median of $218,750 based on 3,817 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $230,000.
Across all AI roles, the market median is $217,500. Top-quartile compensation starts at $272,100. The 90th percentile reaches $325,000. For comparison, the highest-paying categories include AI Safety ($300,000) and Research Engineer ($280,000). By seniority level: Entry: $120,000; Mid: $200,000; Senior: $230,000; Director: $272,150; VP: $250,000.
Fiddler AI AI Hiring
Fiddler AI has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Palo Alto, CA, US.
Location Context
Across all AI roles, 14% (508 positions) offer remote work, while 3,180 require on-site attendance. Top AI hiring metros: New York (1,045 roles, $220,000 median); San Francisco (810 roles, $277,088 median); Los Angeles (397 roles, $215,000 median).
Career Path
Common paths into AI/ML Engineer roles include Data Scientist, Software Engineer, Research Engineer.
From here, career progression typically leads toward ML Architect, AI Engineering Manager, Principal ML Engineer.
The fastest path into ML engineering is through software engineering with a self-directed ML education. A CS degree helps, but production engineering skills matter more than academic credentials. Build something that works, deploy it, and measure it. That portfolio project is worth more than a Coursera certificate. For career growth, the fork comes around the senior level: go deep on technical complexity (staff/principal track) or move into managing ML teams.
What to Expect in Interviews
Expect system design questions around ML pipelines: how you'd build a training pipeline for a specific use case, handle data drift, or design A/B testing infrastructure for model deployments. Coding rounds typically involve Python, with emphasis on data manipulation (pandas, numpy) and algorithm implementation. Take-home assignments often ask you to build an end-to-end ML pipeline from raw data to deployed model.
When evaluating opportunities: Companies that are serious about AI/ML hiring tend to post specific infrastructure details in the job description: the frameworks they use, their model serving stack, their data pipeline tools. Vague postings that just say 'ML experience required' without specifics are often companies that haven't figured out what they need yet.
AI Hiring Overview
The AI job market has 3,708 open positions tracked in our dataset. By seniority: 102 entry-level, 1,705 mid-level, 1,469 senior, and 432 leadership roles (Director, VP, C-Level). Remote roles make up 14% of the market (508 positions). The remaining 3,180 roles require on-site or hybrid attendance.
The market median for AI roles is $217,500. Top-quartile compensation starts at $272,100. The 90th percentile reaches $325,000. Highest-paying categories: AI Safety ($300,000 median, 21 roles); Research Engineer ($280,000 median, 147 roles); AI Architect ($254,798 median, 67 roles).
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
The AI Job Market Today
The AI job market spans 3,708 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (2,605), Data Scientist (310), AI Software Engineer (259). These three account for the majority of open positions, though smaller categories often have higher per-role compensation because of specialized skill requirements.
The seniority mix tells a story about where AI teams are in their maturity. Entry-level roles (102) are outnumbered by mid-level (1,705) and senior (1,469) positions, reflecting that most companies are past the 'build a team from scratch' phase and need experienced engineers who can ship production systems. Leadership roles (Director, VP, C-Level) total 432 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 14% of all AI roles (508 positions), with 3,180 requiring on-site or hybrid attendance. The remote share has stabilized after the post-pandemic correction. Senior and specialized roles (Research Scientist, ML Architect) are more likely to be remote-eligible than entry-level positions, partly because experienced hires have more negotiating power and partly because these roles require less hands-on mentorship.
AI compensation is structured in clear tiers. The market median sits at $217,500. Top-quartile roles start at $272,100, and the 90th percentile reaches $325,000. These figures include base salary with disclosed compensation. Total compensation (including equity, bonuses, and sign-on) runs 20-40% higher at companies that offer those components.
Category matters for compensation. AI Safety roles lead at $300,000 median, while Prompt Engineer roles sit at $140,000. The spread between highest and lowest-paying categories reflects the premium on specialized technical skills versus broader analytical roles.
The most in-demand skills across all AI postings: Python (1,890 postings), Aws (1,103 postings), Azure (877 postings), Rag (855 postings), Gcp (631 postings), Prompt Engineering (560 postings), Pytorch (545 postings), Claude (498 postings). Python dominates, appearing in the vast majority of role descriptions regardless of category. Cloud platform experience (AWS, GCP, Azure) is the second most common requirement. The newer entrants to the top skills list (RAG, vector databases, LLM APIs) reflect the shift from traditional ML toward generative AI applications.
Frequently Asked Questions
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