Do you want to boost your career and collaborate with expert, talented colleagues to solve and deliver against our clients' most important challenges? We are growing and are looking for people to join our team. You'll be part of an entrepreneurial, high-growth environment of over 320,000 employees. Our dynamic organization allows you to work across functional business pillars, contributing your ideas, experiences, diverse thinking, and a strong mindset. Are you ready?
\nThe Role --------
\nWe are looking for highly skilled Data Scientists to join our team. As a Data Scientist, you’ll design and deliver GenAI solutions (LLM/RAG) and applied ML components, taking prototypes through to production with strong evaluation, observability and governance. You will work closely with cross-functional teams, including data engineers, analysts, and business stakeholders, to turn data into actionable strategies that drive business outcomes.
\nKey Responsibilities --------------------
\nRequirements ------------
\nEssential Skills:
\nPreferred Skills:
\nPersonal attributes
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\nLLM & AI Pipeline Engineering - Design, build, and maintain production-grade LLM integration pipelines , including retrieval-augmented generation (RAG), prompt engineering, output parsing, and chain orchestration.
\nDevelop and operate AI features within Jeeves's core financial products: spend categorization, document extraction, anomaly detection, financial Q&A, and automated reconciliation.
\nImplement structured output validation, fallback handling, and confidence scoring to ensure AI decisions meet reliability standards for financial use cases.
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\nOptimize retrieval quality through embedding model selection, chunk strategy, metadata filtering, and re-ranking techniques.
\nML Model Serving & Operations - Collaborate with data scientists to take trained ML models from experimental notebooks to production serving infrastructure.
\nBuild and maintain model serving endpoints with appropriate latency SLOs, input validation, and output monitoring.
\nImplement model performance monitoring and data drift detection to ensure production models remain accurate over time.
\nSupport model retraining workflows by designing clean data pipelines and feature engineering that can be continuously updated.
\nBackend Integration & Reliability - Integrate AI services cleanly with Jeeves's backend microservices , designing clear API contracts, circuit breakers, and graceful degradation patterns.
\nWrite high-quality, testable backend code in Python or Go/Node.js to power AI-integrated features.
\nInstrument AI components with structured logging, distributed tracing, latency dashboards, and alerting to ensure operational visibility.
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\nDevelop and operate AI features within Jeeves's core financial products: spend categorization, document extraction, anomaly detection, financial Q&A, and automated reconciliation.
\nImplement structured output validation, fallback handling, and confidence scoring to ensure AI decisions meet reliability standards for financial use cases.
\nEvaluate and integrate AI frameworks and tools (LangChain, LlamaIndex, OpenAI API, Anthropic API, HuggingFace, vector databases) and advocate for the right tool for the job.
\nEstablish prompt versioning and evaluation practices to ensure AI outputs remain accurate and consistent as models and data evolve.
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\nBuild document ingestion and chunking pipelines for Jeeves's financial data , processing invoices, receipts, policy documents, and transaction records.
\nOptimize retrieval quality through embedding model selection, chunk strategy, metadata filtering, and re-ranking techniques.
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\nBuild and maintain model serving endpoints with appropriate latency SLOs, input validation, and output monitoring.
\nImplement model performance monitoring and data drift detection to ensure production models remain accurate over time.
\nSupport model retraining workflows by designing clean data pipelines and feature engineering that can be continuously updated.
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\nInstrument AI components with structured logging, distributed tracing, latency dashboards, and alerting to ensure operational visibility.
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\nContribute to a culture of quality by writing design docs, reviewing peers' AI system designs, and sharing learnings openly.
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\nDevelop and operate AI features within Jeeves's core financial products: spend categorization, document extraction, anomaly detection, financial Q&A, and automated reconciliation.
\nImplement structured output validation, fallback handling, and confidence scoring to ensure AI decisions meet reliability standards for financial use cases.
\nEvaluate and integrate AI frameworks and tools (LangChain, LlamaIndex, OpenAI API, Anthropic API, HuggingFace, vector databases) and advocate for the right tool for the job.
\nEstablish prompt versioning and evaluation practices to ensure AI outputs remain accurate and consistent as models and data evolve.
\nRetrieval & Vector Search - Design and maintain vector search pipelines using databases such as Pinecone, Weaviate, or pgvector to power semantic search and RAG-based features.
\nBuild document ingestion and chunking pipelines for Jeeves's financial data , processing invoices, receipts, policy documents, and transaction records.
\nOptimize retrieval quality through embedding model selection, chunk strategy, metadata filtering, and re-ranking techniques.
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\nBuild and maintain model serving endpoints with appropriate latency SLOs, input validation, and output monitoring.
\nImplement model performance monitoring and data drift detection to ensure production models remain accurate over time.
\nSupport model retraining workflows by designing clean data pipelines and feature engineering that can be continuously updated.
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\nWrite high-quality, testable backend code in Python or Go/Node.js to power AI-integrated features.
\nInstrument AI components with structured logging, distributed tracing, latency dashboards, and alerting to ensure operational visibility.
\nCollaboration & Growth - Partner with Product, Backend Engineering, and Data Science to define the AI roadmap and translate requirements into reliable systems.
\nContribute to a culture of quality by writing design docs, reviewing peers' AI system designs, and sharing learnings openly.
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\nInvisible Technologies makes AI work. Our end-to-end AI platform structures messy data, automates digital workflows, deploys agentic solutions, measures outcomes, and integrates human expertise where it matters most.
\nOur platform cleans, labels, and structures company data so it is ready for AI. It adapts models to each business and adds human expertise when needed, the same approach we have used to improve models for more than 80% of the world’s top AI companies, including Microsoft, AWS, and Cohere.
\nOur successes span industries, from supply chain automation for Swiss Gear to AI-enabled naval simulations with SAIC, and validating NBA draft picks for the Charlotte Hornets.
\nProfitable for more than half a decade, Invisible reached $134M in revenue and ranked as the number two fastest growing AI company on the 2024 Inc. 5000. In September 2025, we raised $100M in growth capital to accelerate our mission of making AI actually work in the enterprise and to advance our platform technology.
\nAbout The Role
\nAs a Senior Forward Deployed Engineer (FDE) for our U.S. Public Sector team at Invisible, you'll lead high-impact, AI-powered solutions that reshape how our clients operate their most critical workflows. You won’t just build and deploy — you’ll drive the strategy, architecture, and execution of end-to-end systems, working directly with client stakeholders and our internal delivery teams.
\nThis is a hybrid role: equal parts AI architect, hands-on engineer, and technical advisor. You’ll work on the front lines with ambitious clients, turning operational challenges into scalable AI workflows. You’ll be trusted to lead complex engagements, make architectural calls, and mentor others across technical and non-technical domains.
\nWhat You’ll Do
\nWhat We Need
\nWhat’s In It For You
\nInvisible is committed to fair and competitive pay, ensuring that compensation reflects both market conditions and the value each team member brings. Our salary structure accounts for regional differences in cost of living while maintaining internal equity.
\nFor this position, the annual salary ranges by location are:
\nTier 2 Salary Range $164,000 – $240,000USD
\nYou can find more information about our geographic pay tiers here. During the interview process, your Invisible Talent Acquisition Partner will confirm which tier applies to your location. For candidates outside the U.S., compensation is adjusted to reflect local market conditions and cost of living.
\nBonuses and equity are included in offers above entry level. Final compensation is determined by a combination of factors, including location, job-related experience, skills, knowledge, internal pay equity, and overall market conditions. Because of this, every offer is unique. Additional details on total compensation and benefits will be discussed during the hiring process
\nWhat It's Like to Work at Invisible:
\nAt Invisible, we’re not just redefining work—we’re reinventing it. We operate at the intersection of advanced AI and human ingenuity, pushing the boundaries of what’s possible to unlock productivity and scale. Ownership is at the core of everything we do. Here, you won’t just execute tasks—you’ll build, innovate, and shape the future alongside world-class clients pushing the boundaries of AI.
\nWe expect bold ideas, relentless drive, and the ability to turn ambiguity into opportunity. The pace is fast, the challenges are big, and the growth is unmatched. We’re not for everyone, and we’re okay with that. If you’re looking for predictable routines, this isn’t the place for you. But if you’re driven to create, thrive in dynamic environments, and want a front-row seat to the AI revolution, you’ll fit right in.
\n_Country Hiring Guidelines:_ _Invisible is a hybrid organization with offices and team members located around the world. While some roles may offer remote flexibility, most positions involve in-office collaboration and are tied to specific locations. Any location-based requirements will be clearly outlined in the job description._
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