---
title: 'Senior MLOps Engineer at Nace AI'
canonical: 'https://feeny.ai/job/senior-mlops-engineer-nace-ai-palo-alto-x8adzytpzn63'
type: 'job'
last_seen: '2026-09-10'
---

# Senior MLOps Engineer at Nace AI

- **Company:** Nace AI
- **Location:** Palo Alto, CA
- **Employment:** full-time
- **Work type:** onsite
- **Posted:** 2026-07-16
- **Last confirmed live:** 2026-09-10
- **Apply:** https://jobs.ashbyhq.com/nace.ai/61197721-9cc7-45e4-9187-54991d98b497

## Job description

Palo Alto, CA | Full-Time | On-site About Nace AI: Nace AI is an enterprise AI product and research company in Palo Alto (backed by General Catalyst, Walden Catalyst, and Intel). We build long-running AI agents powered by our own specialized SLMs — we started with financial audit and accounting workflows and are expanding from there. Real enterprise deployments, not demos. Role Overview: As a Senior MLOps Engineer, you will own the infrastructure that takes [Nace.AI](http://Nace.AI)'s models from research to reliable, production-grade systems. Our infrastructure generates task-specific Small Language Models (SLMs) in real time — which means our training, serving, and evaluation infrastructure isn't an afterthought; it is the product. You will design and operate the pipelines, orchestration, and serving layers that allow us to train, deploy, monitor, and continuously improve many specialized models at once, with the reliability that high-stakes audit, compliance, and finance workflows demand. This role sits at the intersection of ML engineering, LLM inference infrastructure, and platform reliability, and requires both strong systems instincts and hands-on execution. Key Responsibilities:

- Design, build, and operate end-to-end ML infrastructure: training orchestration, experiment tracking, model registries, CI/CD for models, and automated evaluation pipelines.
- Own LLM/SLM serving infrastructure — scale low-latency, high-throughput inference using frameworks like vLLM, including batching, caching, and autoscaling strategies.
- Build and manage multi-GPU training and inference clusters (scheduling, utilization, cost optimization) across cloud and on-prem environments.
- Implement observability for models in production: latency, throughput, drift, regression, and quality monitoring with actionable alerting.
- Apply inference-time optimizations — quantization (AWQ, GPTQ, FP8/GGUF), distillation support, KV-cache management, and deployment tuning — in partnership with our ML and Research Engineers.
- Harden our stack for enterprise deployment: reproducibility, versioning, access controls, and audit-ready traceability of model behavior.
- Set MLOps best practices and tooling standards as an early, senior member of the infrastructure team.

Qualifications:

- 5+ years of experience in MLOps, ML infrastructure, or platform engineering, with substantial production ownership.
- Proven experience deploying and scaling LLM, inference infrastructure in production, including model serving frameworks such as TRT, vLLM, SGLang or TGI.
- Strong proficiency with Kubernetes, containerization (Docker), and infrastructure-as-code (Terraform or similar).
- Hands-on experience with GPU cluster management and distributed training/serving environments.
- Proficient in Python with a strong track record of building substantial, maintainable systems.
- Experience with ML pipeline and orchestration tooling (e.g., Airflow, Kubeflow, Ray, MLflow, Weights & Biases).
- Solid foundation in computer science fundamentals and cloud architecture (AWS, GCP, or Azure).
- BS degree in CS or related technical field.
- Self-starter comfortable working in a fast-paced, dynamic environment.

Preferred Qualifications:

- MS in CS or related technical field.
- Experience operating multi-node GPU training infrastructure.
- Hands-on experience with quantization techniques (AWQ, GPTQ, FP8/GGUF) and other inference-time optimizations.
- Familiarity with data processing stacks such as Spark and Airflow.
- Experience supporting fine-tuning workflows for LLMs/VLMs (instruction tuning, RLHF/DPO pipelines).
- Experience in regulated or enterprise environments where reliability, security, and auditability are first-class requirements.
- Contributor to open-source ML infrastructure projects.

Why Nace AI?

- Pedigree: Work with a team from top-tier institutions and companies, backed by the best VCs in the world.
- Impact: You are joining early enough to shape the infrastructure foundations of a company aiming to be the "OS" for professional knowledge.
- Competitive Package: Silicon Valley-standard salary, significant equity, and premium benefits.

## About Nace AI

## Company Overview
- **One-liner**: Nace AI builds long-horizon reasoning models and AI agents that audit, reconcile, and report on financial and compliance data for enterprises.
- **Entity Type**: Private (Seed / Series A – $21.5M raised in 2026)
- **Headquarters**: Palo Alto, California, United States
- **Founded**: 2024
- **Founders**: Zhanibek Datbayev (CTO/Co-Founder), Ritesh Shrivastava (Co-Founder), Sudha Valluru (COO/Co-Founder), Swathi B (Co-Founder), and others

## Core Business
- **Primary industries**: Audit, Compliance, Finance, Accounting, Enterprise AI
- **Target customers**: B2B enterprise – specifically audit firms, finance departments, compliance teams, and professional services firms
- **Mission**: Build long-horizon AI that runs organizations; give professionals their time back by automating high-stakes knowledge work

## Products & Services
- **Metamodel**: A specialized small language model (SLM) platform that enterprises can customize with their own policies and knowledge. Used for audit, contract governance, and financial reporting.
- **Agentic Accounting**: AI agent that executes end-to-end financial audits, billing audits, and revenue leakage detection by connecting directly to databases.
- **Nace Verification Intelligence**: Evidence-based reasoning engine that processes up to 1 million files (PDFs, spreadsheets, databases) to automate SOX 404 reports, contract compliance checks, and due diligence.
- **Nace Process Automation**: Automatically maps transaction flows across 10+ disparate databases to visualize and reconcile processes.
- **The Macros**: A research preview product that lets power users create custom AI macros for repetitive professional tasks.

## Market Standing
- **Valuation/Market Cap**: Not publicly disclosed
- **Key Metric**: Total funding raised – $21.5M (May 2026 round led by Walden Catalyst Ventures, with participation from General Catalyst, AME Cloud Ventures, AICONIC VENTURES); earlier $5M seed round (April 2025) led by General Catalyst.
- **Notable Investors/Partners**: General Catalyst, Walden Catalyst Ventures, AME Cloud Ventures, AICONIC VENTURES
- **Growth Signals**: 
  - Launched from stealth in March 2025 with Metamodel 1
  - Raised $21.5M within 14 months of founding
  - Operations in 7 countries (US, Kazakhstan, India, Canada, Germany, Ukraine, Georgia)
  - 2,574 LinkedIn followers (+141% YoY)
  - Active job postings for senior roles (Product Manager, Audit Consultant, Growth Lead)

## Competitive Advantages
- **Metamodel approach**: Enterprises can turn internal policies and employee knowledge into custom, on‑premise AI models, enforcing consistency and accuracy.
- **Evidence‑based reasoning**: The system explains exactly where it looked and why – building trust in regulated environments (SOC 2 Type II, AICPA audited infrastructure).
- **Small model efficiency**: Uses specialized small language models (SLMs) instead of massive general LLMs, making deployments faster, cheaper, and compliant with data sovereignty requirements.
- **End‑to‑end automation**: Covers the entire audit workflow – from raw database ingestion to boardroom‑ready reports – without any manual spreadsheet work.

## Strategic Focus
- **Scale the Metamodel platform**: Turn enterprise policies into custom AI agents for every department.
- **Expand into adjacent compliance verticals** (tax, due diligence, contract governance).
- **Grow the team** across engineering, product, and go‑to‑market roles (currently 39–50 employees, with a heavy technical tilt of ~45%).
- **Maintain on‑premises / hybrid cloud deployments** to meet strict regulatory requirements of audit and finance clients.

## Why Work Here
- **Culture**: Fast‑paced research‑first environment assembling a “world‑class team” to solve high‑stakes scaling challenges in AI reasoning.
- **Work model**: Hybrid workspace (Palo Alto offices); many roles are in‑office for close collaboration on complex ML and product work.
- **Team composition**: Heavy technical concentration (45% of staff are engineers/researchers), with deep domain expertise from ex‑Dropbox, Bloomberg, McKinsey, and AI labs.
- **Impact**: Build infrastructure that gives audit, compliance, and finance professionals their time back – directly reduces manual drudgery in multi‑billion‑dollar compliance markets.
- **Perks**: Not explicitly detailed, but the company emphasizes “autonomy, impact, and working on the hardest AI problems in enterprise.”

## Sources
1. [nace.ai](https://nace.ai/)
2. [nace.ai/careers](https://nace.ai/careers)
3. [LinkedIn – Nace AI](https://www.linkedin.com/company/nace-ai)
4. [Built In – Nace.AI](https://builtin.com/company/nace-ai)
5. [Jobs.ashbyhq.com – Nace AI](https://jobs.ashbyhq.com/nace.ai)

## Other roles at Nace AI

- [VP of Engineering](https://feeny.ai/job/vp-of-engineering-nace-ai-palo-alto-q8d26ckgppqq) — Palo Alto, CA
- [Technical Program Manager](https://feeny.ai/job/technical-program-manager-nace-ai-palo-alto-8py4nbage3rw) — Palo Alto, CA
- [Senior Product Designer (AI & Prototyping)](https://feeny.ai/job/senior-product-designer-ai-prototyping-nace-ai-palo-alto-g533v3r04kqy) — Palo Alto, CA
- [Financial Audit Consultant](https://feeny.ai/job/financial-audit-consultant-nace-ai-palo-alto-xd0vjpcf326t) — Palo Alto, CA
- [Senior Technical Recruiter](https://feeny.ai/job/senior-technical-recruiter-nace-ai-palo-alto-w9tkfpeqqv2f) — Palo Alto, CA
- [Growth Marketing Lead](https://feeny.ai/job/growth-marketing-lead-nace-ai-palo-alto-7bw1bwfdqydv) — Palo Alto, CA
- [Valuation Due Diligence Expert](https://feeny.ai/job/valuation-due-diligence-expert-nace-ai-palo-alto-c51g3m2xy7q0) — Palo Alto, CA
- [SOX Audit Expert](https://feeny.ai/job/sox-audit-expert-nace-ai-palo-alto-3tznenh4xa5w) — Palo Alto, CA
- [Senior Product Manager](https://feeny.ai/job/senior-product-manager-nace-ai-palo-alto-vsa8p0mak591) — Palo Alto, CA
- [Tax Consultant](https://feeny.ai/job/tax-consultant-nace-ai-palo-alto-hryt0rr498zw) — Palo Alto, CA