---
title: 'Machine Learning Engineer at AI Squared'
canonical: 'https://feeny.ai/job/machine-learning-engineer-ai-squared-washington-vmgm28v6vjt2'
type: 'job'
last_seen: '2026-09-11'
---

# Machine Learning Engineer at AI Squared

- **Company:** AI Squared
- **Location:** Washington, DC
- **Posted:** 2025-09-24
- **Last confirmed live:** 2026-09-11
- **Apply:** https://job-boards.greenhouse.io/aisquared/jobs/4604010006

## Job description

Machine Learning Engineer

Washington, DC (Hybrid)

About the Role:

We are seeking a highly skilled Machine Learning Engineer to join our core AI team. In this role, you will focus on deploying, maintaining, and monitoring the AI/ML systems that power our platform. You will work closely with data scientists, data engineers, and product teams to ensure scalable, reliable, and production-grade AI solutions. You’ll play a critical role in operationalizing large language models (LLMs) and other ML systems, ensuring they run efficiently, securely, and with robust monitoring in place.

Key Responsibilities:

- Design, implement, and maintain ML deployment pipelines for scalable production systems.
- Operationalize large language models (LLMs) and other AI/ML models, ensuring high availability and reliability.
- Build robust model monitoring, logging, and alerting systems to track performance and detect drift.
- Partner with data scientists to transition models from research/prototype into production-ready deployments.
- Develop CI/CD pipelines for ML workflows, integrating testing, validation, and automated deployment.
- Optimize runtime performance of ML models across cloud platforms (AWS, GCP, Azure) and distributed systems.
- Apply containerization and orchestration (Docker, Kubernetes) to enable reproducible, scalable systems.
- Collaborate with cross-functional teams to ensure ML systems align with platform goals and business requirements.

Qualifications:

- 5+ years of experience as a Machine Learning Engineer, MLOps Engineer, or similar role.
- Proven experience deploying and maintaining machine learning models in production at scale.
- Hands-on experience with ML lifecycle tooling (MLflow, Kubeflow, SageMaker, Vertex AI, or similar).
- Strong proficiency in Python; familiarity with ML frameworks such as PyTorch or TensorFlow.
- Deep knowledge of containerization (Docker) and orchestration (Kubernetes) for production ML systems.
- Expertise with cloud platforms (AWS, GCP, Azure) for ML deployment and scaling.
- Strong understanding of MLOps best practices, monitoring, and automation.
- Excellent problem-solving skills, with an emphasis on building reliable, scalable systems.
- Strong communication and collaboration skills across technical and non-technical teams.

## About AI Squared

## Company Overview
- **One-liner**: AISquared provides a secure, low-code platform to operationalize AI by embedding insights directly into enterprise business applications and workflows.
- **Entity Type**: Private (Series A)
- **Headquarters**: Mountain View, California, United States
- **Founded**: 2021
- **Founders**: Napoleon Paxton, Ph.D. (Co-Founder & Chief Data Scientist); Jacob Renn, Ph.D. (Co-Founder & Chief Data Scientist)

## Core Business
- Primary industry/industries: Enterprise AI Infrastructure, Software Development
- Target customers: B2B, Enterprise, Federal/GovCon, Mid-Market, Growth Teams
- Mission or purpose statement: "Shaping the Future of AI" by bridging the gap between cutting-edge AI and real-world impact, ensuring organizations don’t just adopt AI, but thrive with it.

## Products & Services
- **UNIFI**: Enterprise-grade platform for scaling AI beyond pilots. Connects data sources and AI models, orchestrates workflows, secures deployments with defense-grade controls, and embeds AI insights directly into systems like Salesforce, ServiceNow, and Slack.
- **Sparx**: Agentic AI solution for growing businesses, designed for teams that need to operationalize AI without a major infrastructure overhaul.
- **100+ Pre-built Connectors**: Library of connectors across data sources and LLMs, enabling one-click integration to existing data and AI infrastructure.

## Market Standing
- **Valuation/Market Cap**: Not publicly disclosed
- **Key Metric**: Total Funding of $19.8M; Annual Revenue of $2.5M (per LinkedIn)
- **Notable Investors/Partners**: Ansa Capital (led Series A), New Enterprise Associates (NEA) (led Seed Round); Board includes Greg Papadopoulos, Ph.D. (Venture Partner at NEA), Roger W. Ferguson, Jr. (Former President & CEO at TIAA), and Allan Jean-Baptiste (Co-Founder & Managing Partner at Ansa Capital).
- **Growth Signals**: Acquired Multiwoven in May 2024; trusted by leading financial institutions, complex supply chain organizations, and the United States Department of Defense; LinkedIn followers grew 50.2% year-over-year to 8,193; operates in 5 countries.

## Competitive Advantages
- **Zero Trust Security for Enterprise AI**: Role-based access, audit trails, and encrypted data flows ensure AI operates only within defined boundaries, making it suitable for federal and highly regulated environments.
- **The "Last Mile" Solution**: Directly addresses the common failure point of AI deployment by embedding insights into the business applications where work actually happens, without requiring large engineering lifts.
- **Measurable ROI**: Provides leaders with visibility into adoption, performance, and business impact, claiming 5x faster time-to-value.

## Strategic Focus
- **Scaling AI Adoption**: Moving organizations from stalled pilots to real operational impact.
- **Federal & Enterprise Expansion**: Specifically targeting secure, mission-critical environments with dedicated Federal sales and engineering teams.
- **Platform Ecosystem**: Building out a comprehensive platform (UNIFI + Sparx) that covers the full lifecycle of AI integration, from connection to continuous improvement via user feedback.

## Why Work Here
- **Culture**: Mission-driven, innovation-first, with a focus on "impact over hype." Values include relentless curiosity and security & trust.
- **Hybrid Work Policy**: Flexible model empowering employees to work from home or in offices, with in-person collaboration for key moments of innovation.
- **Offices**: Two vibrant hubs — Mountain View, CA (West Coast HQ for engineering, product, and AI research) and Bangalore, India (cutting-edge AI infrastructure development). Also has a Washington, DC office for federal work.
- **Benefits**: Comprehensive health coverage (medical, dental, vision), equity & competitive compensation, 401(k) matching, generous unlimited PTO, paid parental leave, wellness reimbursements, and employee assistance programs.
- **Engineering Culture**: Focus on building the infrastructure for agentic enterprise AI with a small, fast-growing team (52 employees). Open roles include Backend Engineer, Machine Learning Engineer, Data Scientist, and QA Engineer.

## Sources
1. [aisquared.ai](https://aisquared.ai/)
2. [aisquared.ai/careers](https://aisquared.ai/careers/)
3. [aisquared.ai/about](https://aisquared.ai/about/)
4. [job-boards.greenhouse.io/aisquared](http://job-boards.greenhouse.io/aisquared)
5. [linkedin.com/company/aisquaredinc](https://www.linkedin.com/company/aisquaredinc)

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