How to Apply to Fiddler Ai

9 min read Last updated April 20, 2026 8 open positions
Blake Crosley
Blake Crosley Researched using publicly available information, hiring data, and ATS analysis.

Key Takeaways

  • Fiddler AI is a Series B ML observability startup pivoting hard toward LLM monitoring to stay relevant in a consolidating category.
  • The company has raised roughly seventy-five million dollars total, employs around one hundred people, and is headquartered in Palo Alto with a Bangalore engineering hub.
  • The Ashby-based hiring process is structured, senior-staffed, and technically rigorous. Expect four to eight weeks from application to offer.
  • Regulated-industry experience, production ML systems, and explainability depth are the most valuable signals on a Fiddler resume.
  • The competitive environment is crowded, venture funding is tight in 2024-2025, and candidates should evaluate equity and runway realistically, not optimistically.
  • Responsible AI, NIST AI RMF, EU AI Act, and SR 11-7 positioning differentiate Fiddler from pure MLOps competitors and are worth studying before interviews.
  • Founders Krishna Gade and Amit Paka are actively involved in hiring, and authentic answers beat rehearsed ones in behavioral rounds.
  • No IPO is imminent. Joining Fiddler is a bet on enterprise depth, category staying power, and the LLM observability pivot landing.

About Fiddler Ai

Fiddler AI is an enterprise AI observability startup headquartered in Palo Alto, California, with an engineering hub in Bangalore, India. Founded in 2018 by Krishna Gade (formerly engineering leadership at Facebook's News Feed, Pinterest, and Twitter) and Amit Paka, the company set out to solve a problem most machine learning teams discovered the hard way: models degrade silently in production, and nobody notices until a regulator, a customer, or a journalist does. Fiddler built one of the first commercial platforms for model monitoring, explainability, and bias detection aimed squarely at regulated industries where "the model said so" is not an acceptable answer. The product is the Fiddler AI Observability Platform. It covers traditional ML monitoring, drift detection, data quality, performance tracking, and explainability through techniques like SHAP values and integrated gradients, along with fairness and bias analysis. Since 2023, Fiddler has leaned heavily into LLM observability, shipping capabilities for hallucination detection, prompt drift, toxicity and jailbreak monitoring, RAG evaluation, and the Fiddler Auditor benchmark tooling. This pivot is essential. The MLOps observability category is crowded, and the center of gravity has shifted toward generative AI trust and safety. Fiddler has raised approximately seventy-five million dollars in total. The Series B round, led by Insight Partners in 2022, added around thirty-two million dollars, with prior investors including Amplify Partners, Lightspeed Venture Partners, and Lockheed Martin Ventures. The company employs roughly one hundred people, split between Palo Alto headquarters and Bangalore. Its customer base skews enterprise and regulated: the US Air Force, NASA, Tradeweb, and a range of banks, insurers, and healthcare organizations. Positioning around NIST AI Risk Management Framework, the EU AI Act, and Federal Reserve SR 11-7 model risk guidance is deliberate and is one of the clearer differentiators against pure MLOps rivals. The competitive picture is crowded. Weights and Biases was acquired by CoreWeave in 2025. Truera was acquired by Snowflake in 2024. Arize AI, WhyLabs, Evidently AI, Aporia, Credo AI, Galileo, LangSmith, Humanloop, and Langfuse all overlap in various slices of the stack. Candidates should understand that Fiddler is a Series B company operating in a consolidating market, that no IPO is imminent, and that the 2024-2025 venture environment has raised legitimate questions about runway and growth trajectory for companies at this stage. The bet is on enterprise depth and responsible AI positioning rather than developer-first adoption.

Application Process

  1. 1
    Apply through Fiddler's Ashby-hosted job board at jobs

    Apply through Fiddler's Ashby-hosted job board at jobs.ashbyhq.com/fiddler. Filter by Palo Alto, Bangalore, or remote tags depending on the role's listed location.

  2. 2
    Expect a recruiter screen within one to two weeks that covers your background, i

    Expect a recruiter screen within one to two weeks that covers your background, interest in ML observability and responsible AI, and compensation expectations.

  3. 3
    Technical screens vary by role: engineering candidates face a coding round focus

    Technical screens vary by role: engineering candidates face a coding round focused on Python, data structures, or ML systems; ML and research roles include a technical depth discussion on monitoring, explainability, or LLM evaluation.

  4. 4
    Plan for a take-home assignment or a pair-programming exercise for senior engine

    Plan for a take-home assignment or a pair-programming exercise for senior engineering and ML roles. Fiddler tends to prefer realistic tasks over abstract puzzles.

  5. 5
    Expect an onsite or virtual loop of four to six interviews covering coding, syst

    Expect an onsite or virtual loop of four to six interviews covering coding, system design, ML depth if applicable, behavioral, and an executive or founder conversation.

  6. 6
    Prepare to discuss how you think about model risk, bias, fairness, and LLM safet

    Prepare to discuss how you think about model risk, bias, fairness, and LLM safety. These are core product concerns and interviewers will probe your intuition.

  7. 7
    Timeline from application to offer typically runs four to eight weeks

    Timeline from application to offer typically runs four to eight weeks. Bangalore hiring loops often move faster than Palo Alto due to time zone concentration.

  8. 8
    Offers include equity at Series B valuation

    Offers include equity at Series B valuation. Negotiate strike price, vesting cliff, and refresh schedule, not just base salary.

  9. 9
    Reference checks are standard and thorough

    Reference checks are standard and thorough. Expect backchannel conversations if you have mutual contacts in the ML observability or MLOps community.

  10. 10
    Decisions are typically communicated within one week after the final loop

    Decisions are typically communicated within one week after the final loop. If you hear nothing after two weeks, a polite nudge to the recruiter is reasonable.

Resume Tips for Fiddler Ai

recommended

Lead with concrete ML or production systems work

Lead with concrete ML or production systems work. Generic "AI enthusiast" language does not survive a Fiddler screen. Specificity wins.

recommended

Quantify model monitoring, drift detection, or observability work you have done

Quantify model monitoring, drift detection, or observability work you have done. Latency numbers, throughput, false positive rates, and incident reductions are the right metrics.

recommended

Name the frameworks and tools you have used in production: PyTorch, TensorFlow,

Name the frameworks and tools you have used in production: PyTorch, TensorFlow, scikit-learn, Ray, Kubernetes, Kafka, Spark, Snowflake, Databricks, and LLM-adjacent stacks like LangChain, LlamaIndex, or vLLM.

recommended

If you have worked with explainability techniques like SHAP, LIME, integrated gr

If you have worked with explainability techniques like SHAP, LIME, integrated gradients, or counterfactuals, say so explicitly. These map directly to Fiddler's product surface.

recommended

Highlight any regulated-industry experience

Highlight any regulated-industry experience. Banking, insurance, healthcare, and government ML work is a differentiator given Fiddler's customer base.

recommended

For LLM and generative AI work, be honest about depth

For LLM and generative AI work, be honest about depth. Prompt engineering is not the same as evaluation infrastructure, and interviewers will tell the difference.

recommended

Keep the resume to one or two pages

Keep the resume to one or two pages. Ashby parses well but recruiters still skim. Put the load-bearing evidence in the top third of the page.

recommended

Avoid buzzword soup

Avoid buzzword soup. "Leveraged synergies across AI initiatives" tells Fiddler nothing. "Reduced model drift false alarms by forty percent by tuning KS-test thresholds" tells them everything.

recommended

Include open-source contributions, papers, or talks if you have them

Include open-source contributions, papers, or talks if you have them. The Palo Alto team skews senior and values public technical output.

recommended

Tailor the summary to the specific role

Tailor the summary to the specific role. A platform engineering resume and an ML research resume should not look the same, even if the underlying experience overlaps.

ATS System: Ashby

Fiddler AI uses Ashby as its applicant tracking system, served at jobs.ashbyhq.com/fiddler. Ashby is a modern ATS popular with Series A through C startups. It parses PDFs and Word documents reliably, supports structured fields, and does not penalize clean single-column resumes.

  • Submit a PDF unless the job posting specifically requests another format. Ashby's PDF parser is strong and preserves formatting.
  • Use a clean single-column layout. Two-column resumes sometimes scramble section order in ATS parsing even when humans read them fine.
  • Match keywords from the job description naturally: Python, PyTorch, MLOps, model monitoring, observability, LLM, Kubernetes, AWS, GCP, and the specific responsibilities listed.
  • Fill out Ashby's structured fields completely. Linking your LinkedIn, GitHub, and portfolio in the dedicated fields surfaces them to recruiters more reliably than burying them in the resume.
  • The cover letter field is optional but read. A short, specific paragraph about why Fiddler specifically, not AI observability generally, beats a generic template.
  • Do not upload scanned image PDFs. Ashby cannot extract text from images and your resume will appear empty to the recruiter.
  • Avoid tables, text boxes, and headers or footers with contact information. Put contact details inline at the top of the document.

Interview Culture

Fiddler's interview culture reflects its founders' backgrounds: technically rigorous, pragmatic, and oriented around real systems rather than whiteboard theater.

Interviewers tend to be senior individual contributors and staff engineers from the Palo Alto office, with Bangalore team members joining for most loops. Expect conversations that probe how you reason under ambiguity more than whether you can recite algorithm trivia. Coding rounds lean Python and data-heavy. Questions often involve manipulating model outputs, computing statistical metrics, or debugging a broken observability pipeline rather than pure Leetcode. System design rounds focus on streaming data, time series storage, large-scale model inference, and real-time alerting. If you have built anything that monitors production ML, be ready to describe exactly what broke and how you fixed it. ML and research rounds drill into fundamentals: drift detection methods, fairness metrics, explainability techniques, and increasingly, LLM evaluation. Fiddler cares about whether you can distinguish a PSI score from a KS test, whether you understand the limits of SHAP on non-tabular data, and whether you have opinions about hallucination benchmarks. Hand-waving does not land well. The behavioral portion is unusually substantive. Founders Krishna Gade and Amit Paka are involved in senior hiring, and the loop frequently includes a conversation with one of them. Expect questions about why you want to work on responsible AI specifically, how you handle disagreement with a PM or a customer, and what your long-term career arc looks like. Answers that sound rehearsed do worse than honest ones, including about past mistakes. Culturally, Fiddler is startup-paced but not chaotic. The Palo Alto office trends toward experienced hires, while Bangalore has a mix of senior and mid-level engineers. Hindi and Kannada are common in Bangalore but English is the working language across the company. Dress is casual. Compensation is competitive for Series B but not FAANG-matching, and candidates should evaluate equity realistically given the current venture environment.

What Fiddler Ai Looks For

  • Depth in machine learning systems, not just notebook-level ML. Production experience counts more than Kaggle rankings.
  • Genuine interest in responsible AI, model risk, and the regulatory landscape. Candidates who treat this as window dressing do not advance.
  • Ability to communicate technical tradeoffs clearly, especially to non-ML stakeholders. Fiddler's customers often include risk officers and compliance teams.
  • Comfort with ambiguity. The LLM observability category is still being defined and roles shift as the product evolves.
  • Pragmatism over perfectionism. Fiddler ships to enterprise customers and values engineers who can make tradeoffs and move.
  • Solid software engineering fundamentals. ML teams that cannot write maintainable code become liabilities at scale.
  • Curiosity about the competitive landscape. Candidates who understand where Fiddler sits against Arize, WhyLabs, Evidently, and Galileo come across as serious.
  • Enterprise sensibility. Experience selling to or supporting regulated industries, or willingness to learn that mode, is a real plus.
  • Collaboration across time zones. Palo Alto and Bangalore need to work together, and candidates who have done this well before have an edge.
  • Low ego. The interview loop includes founders and senior ICs who notice when candidates posture. Earned confidence lands. Performed confidence does not.

Frequently Asked Questions

Is Fiddler AI a stable place to work given the crowded ML observability market?
Fiddler is Series B with roughly seventy-five million dollars raised and an enterprise customer base that includes the US Air Force, NASA, and major financial institutions. That gives it real revenue and a defensible niche in regulated industries. At the same time, the category is consolidating, Truera was acquired by Snowflake in 2024, Weights and Biases by CoreWeave in 2025, and venture conditions in 2024-2025 are tight. Stability is reasonable but not guaranteed. Evaluate equity conservatively.
What ATS does Fiddler AI use and how should I optimize for it?
Fiddler uses Ashby at jobs.ashbyhq.com/fiddler. Submit a clean single-column PDF, fill out all structured fields including LinkedIn and GitHub, and match keywords from the job description naturally. Ashby parses well so there is no need to strip formatting aggressively, but avoid tables, text boxes, and image-based PDFs.
Does Fiddler AI hire remote, or do I need to be in Palo Alto or Bangalore?
Most engineering roles are posted as Palo Alto or Bangalore. Some roles are remote-friendly within the US or India, and a few customer-facing roles support other US cities. Check the specific job posting. Fully remote international hiring is uncommon.
How hard are the technical interviews at Fiddler AI?
Rigorous but practical. Coding rounds lean toward Python and data manipulation over pure algorithms. System design focuses on streaming, time series, and real-time alerting. ML rounds drill into drift, fairness, explainability, and LLM evaluation. Candidates with production ML experience tend to outperform pure research backgrounds.
What is the compensation range at Fiddler AI?
Fiddler pays competitively for a Series B company but does not match FAANG total compensation. Base salaries are market-rate for Palo Alto and Bangalore, and equity is Series B valuation. Negotiate vesting, refresh grants, and strike price in addition to base. Ask about preferred stock terms if you want to understand equity realistically.
Is Fiddler AI pivoting away from traditional ML observability toward LLMs?
Fiddler is expanding into LLM observability, not abandoning traditional ML monitoring. The 2024-2025 product roadmap emphasizes hallucination detection, prompt drift, RAG evaluation, and safety benchmarks, but the core ML monitoring, explainability, and fairness features remain central. The pivot is additive and necessary for category relevance.
What does the work culture look like day-to-day?
Startup-paced but not chaotic. The Palo Alto office skews senior and experienced. Bangalore is a mix of senior and mid-level engineers. Working language is English across the company. Meetings span time zones, and strong async communication helps. Expect real ownership of features and close proximity to customers.
How should I prepare for the behavioral round, especially with founders?
Be honest and specific. Krishna Gade and Amit Paka are experienced operators and detect rehearsed answers easily. Have concrete examples ready for questions about disagreement, failure, and customer empathy. Understand why responsible AI matters to you personally, because generic mission-alignment pitches do not land.
What kinds of roles does Fiddler AI hire for?
Software engineering across backend, platform, data, and frontend. ML engineering with monitoring, explainability, or LLM focus. Research scientists for bias, fairness, and LLM evaluation. Solutions engineering and customer engineering for enterprise deployments. Product management, sales, and marketing at smaller volume. Check Ashby for current openings.
How long does the hiring process take from application to offer?
Typically four to eight weeks. Recruiter screen within one to two weeks of applying. Technical screens and take-homes over the next two to three weeks. Onsite loop of four to six interviews, often virtual. Offer within one week after the final round. Bangalore loops often move faster than Palo Alto.
What is the biggest risk of joining Fiddler AI in 2026?
Category crowding and consolidation. ML observability has too many vendors and enterprise buyers are rationalizing their tool stacks. Fiddler's enterprise and regulated-industry positioning is defensible, but the 2024-2025 venture environment is unforgiving for Series B companies that do not demonstrate clear path to efficient growth. The LLM observability pivot has to land. Candidates should ask direct questions about revenue growth, net retention, and runway.

Open Positions

Fiddler Ai currently has 8 open positions.

Check Your Resume Before Applying → View 8 open positions at Fiddler Ai

Related Resources

Similar Companies

Sources

  1. Fiddler AI Careers - Ashby Job Board —
  2. Fiddler AI Official Website —
  3. Fiddler AI Raises $32M Series B Led by Insight Partners —
  4. Krishna Gade LinkedIn Profile —
  5. NIST AI Risk Management Framework —
  6. EU AI Act Overview - European Commission —
  7. Federal Reserve SR 11-7 Guidance on Model Risk Management —
  8. Snowflake Acquires Truera - Snowflake Press Release —
  9. CoreWeave Acquires Weights & Biases —
  10. Ashby ATS Product Documentation —
  11. Fiddler Auditor GitHub Repository —
  12. Insight Partners Portfolio - Fiddler AI —