---
title: 'Machine Learning Engineer I - Message Security Products at Abnormal'
canonical: 'https://feeny.ai/job/machine-learning-engineer-i-message-security-products-abnormal-singapore-0f4s57k5mmpf'
type: 'job'
last_seen: '2026-09-11'
---

# Machine Learning Engineer I - Message Security Products at Abnormal

- **Company:** Abnormal
- **Location:** Singapore
- **Work type:** remote
- **Posted:** 2026-07-27
- **Last confirmed live:** 2026-09-11
- **Apply:** https://abnormal.ai/careers/jobs/7816935003?gh_jid=7816935003

## Job description

## About the Role

Abnormal AI is seeking a Machine Learning Engineer - I (MLE) to join the Misdirected Email Detection (MED) team. The MED team plays a critical role in preventing accidental data loss by detecting and blocking misdirected outbound emails, delivering protection at scale without adding operational burden to customer SOCs.

This is a highly applied role for MLEs who thrive on building, iterating, and experimenting. Rather than focusing solely on model training, you will also be responsible for developing practical, end-to-end ML solutions. This includes but is not limited to generating and refining features, testing hypotheses, averaging signals, and translating research ideas into production-grade systems, all while collaborating cross-functionally to turn customer needs into measurable product improvements. The ideal candidate combines a tinkerer's mindset with technical rigor, balancing innovation with production excellence to drive experimentation, scale solutions, and deliver reliable detection capabilities that create meaningful customer impact in real-world environments.

## What you will do

- Partner with Product Manager, Tech Lead and engineering stakeholders to align technical deliverables to roadmap milestones and ensure successful GA launches across supported environments.
- Own the full ML lifecycle for Misdirected Email, including data wrangling, feature engineering, model training and evaluation, deployment, and monitoring. Deliver iterative improvements with measurable reliability and customer impact.
- Run rigorous experiments and evaluations (offline metrics, online A/B testing, post-launch monitoring), set thresholds, and conduct targeted error analysis to prevent regressions.
- Communicate effectively across time zones, maintain high-quality technical documentation, and contribute to shared team knowledge.
- Participate in shared on-call rotation for owned components, with responsibilities focused on detection efficacy and realtime scoring systems. Priorities include resolving efficacy-related alerts, investigating high-visibility false positives, and addressing reported false positives/false negatives from customers or internal teams.

Must Haves

- BS degree in Computer Science, Machine Learning, Artificial Intelligence, Information Systems, or a related engineering or quantitative field.
- 1+ years building and operating applied ML features in production systems.
- Proven experience contributing to end-to-end ML systems, including data wrangling (text and structured), feature engineering, model selection, training, evaluation, and production deployment with monitoring.
- Demonstrated ability to implement and reason about algorithms, develop features, average and combine signals, and apply numerical computing effectively.
- Demonstrated ability to interrogate production data, identify behavioral or trend shifts, and launch targeted experiments to improve model efficacy.
- Understanding of online vs offline pipelines, data tables and labeling workflows to effectively leverage tooling to support safe, scalable model deployments.
- Experience running offline metrics, online A/B tests, setting thresholds, and monitoring drift and performance, with guardrails and rollback strategies to ensure reliable iteration.
- Strong written and asynchronous communication skills. Effective working independently and across distributed, cross-functional teams.

## Nice to Have

- Experience with our stack: Python, Go, AWS, Spark, Databricks
- Experience in email security/DLP or misdirected email prevention domains and customer-focused ML deployments.
- Experience writing detectors/rules to complement ML models for safe launches and rapid iteration.
- Experience with operationalising research into reliable, customer-facing systems, with emphasis on scalability, performance, and detection accuracy in real-world environments.
- Prior experience contributing to a small team or project to deliver a feature or component from scratch.

## #LI-UC1

A note on AI in our process: Abnormal AI uses AI-assisted tools to help our recruiting team prepare for candidate interviews. These tools analyze resume content and role requirements to suggest interview questions and areas for the interviewer to explore.They do not make hiring decisions or screen candidates automatically. Every decision about a candidacy is made by a person. Further, if your application is successful and Abnormal AI makes a conditional offer of employment, we will carry out pre-employment checks which must be successfully completed to progress to a final offer. All processes and pre-employment checks are in line with prevailing legislation and Abnormal AI's policies relevant to our security and privacy standards.

Abnormal AI is an equal opportunity employer. Qualified applicants will receive consideration for employment without regard to race, color, religion, sex, national origin, disability, protected veteran status or other characteristics protected by law. For our EEO policy statement please [click here](https://assets.contentstack.io/v3/assets/blt3a076cfd9753fa1f/blt9cbda641db7c0045/6a1e3d3a66aea075cd33e462/AAP_and_EEO_Statement.pdf). If you would like more information on your EEO rights under the law, please [click here](https://assets.contentstack.io/v3/assets/blt3a076cfd9753fa1f/blt4eb3a265cc7b1ca9/6a1e3d3bfe33e73c16ff026b/EEOC_Know_Your_Rights_Oct_30_2025.pdf).

## About Abnormal

## Company Overview
- **One-liner**: Abnormal Security uses behavioral AI to autonomously detect and stop sophisticated email attacks, account takeovers, and insider threats for enterprises.
- **Entity Type**: Private (Series D)
- **Headquarters**: San Francisco, California, USA
- **Founded**: 2018
- **Founders**: Evan Reiser (CEO) and Sanjay Jeyakumar (CTO)

## Core Business
- **Primary industry**: Cybersecurity (Cloud Email Security, Identity Security, AI Security)
- **Target customers**: B2B, Enterprise (over 25% of the Fortune 500)
- **Mission statement**: "Stop crime with AI and bring cybersecurity from the future to defenders today."

## Products & Services
- **Abnormal Behavioral Security Platform**: An AI-native, cloud-based platform that builds behavioral baselines for every user and entity in an organization to detect anomalies that indicate attacks.
  - **Email Security**: Autonomous detection and remediation of BEC, phishing, and account takeover attacks via API-based, cloud-native architecture. Deploys in 60 seconds with no MX changes.
  - **Identity Security**: Harden identities, detect compromise, and prevent insider threats by analyzing behavioral signals in real-time.
  - **Insider Threat**: Blocks fraudulent actors and malicious insiders by autonomously addressing anomalies.
  - **AI Security (Attune AI)**: Provides visibility and policy enforcement for all AI tools used within an organization, enabling AI governance.

## Market Standing
- **Valuation/Market Cap**: Not publicly disclosed (last reported Series D valuation was $2.6B in 2022)
- **Key Metric**: Over 25% of the Fortune 500 are customers. The company has stopped millions of attacks and saved billions in financial losses.
- **Notable Investors/Partners**: Insight Partners, Greylock Partners, Menlo Ventures, CrowdStrike Falcon Fund (strategic investor)
- **Growth Signals**: The company is AI-native from day one, with revenue growing rapidly. It is expanding its platform from email security into identity, insider threat, and AI governance. Headcount has grown significantly, and the company continues to hire across engineering, GTM, and operations.

## Competitive Advantages
- **Behavioral AI Moat**: Unlike signature-based or rules-based security tools, Abnormal builds a unique behavioral baseline for every organization and user. This allows it to detect novel attacks that no other vendor sees.
- **AI-Native Operations**: The company doesn't just build AI products—it runs its entire business (product, CS, finance, HR) with AI agents, compounding its speed of innovation.
- **Deep Enterprise Trust**: Over 25% of the Fortune 500 give Abnormal the authority to autonomously make security decisions, a level of trust very few vendors achieve.
- **Founding Team Origins**: Founders came from adtech (building anomaly-detection AI at scale) rather than traditional cybersecurity, giving them an outsider's perspective that led to the behavioral AI approach.

## Strategic Focus
- **Platform Expansion**: Moving beyond email security into identity, insider threat, and AI security, building a unified behavioral security platform.
- **AI-First Product Development**: Using AI agents to compress product development cycles from quarters to weeks, rapidly launching new products.
- **Operational Efficiency**: Embedding AI across every internal function to drive automation and efficiency, allowing the company to scale without linear headcount growth.
- **Market Leadership**: Capitalizing on the wave of AI-generated attacks to position behavioral AI as the only viable defense.

## Why Work Here
- **Remote-First Culture**: From day one, Abnormal has been remote-first. Employees have the choice to work from home, from hub offices, or from co-working spaces (WeWork). Async work is the default, with deliberate synchronous overlap time. [abnormal.ai](https://abnormal.ai/careers/culture)
- **High Autonomy, High Accountability**: No micromanagement. Clear expectations. Growth is based on impact, not tenure or title. "You don't wait your turn." [abnormal.ai](https://abnormal.ai/careers/culture)
- **AI-Native Work Environment**: Every employee, regardless of role, gets access to the best AI tools and is expected to use them. The company rebuilds work processes around what AI makes possible—not by bolting AI onto old workflows. [abnormal.ai](https://abnormal.ai/careers/culture)
- **Compressed Growth Timeline**: Because everything moves fast with AI, impact that would take quarters elsewhere happens in weeks. One person can do what used to take a team. Outsized impact is rewarded proportionally. [abnormal.ai](https://abnormal.ai/careers/culture)
- **Transparent and Direct Culture**: "We say what's true, even when it's uncomfortable." Leaders are accessible, teams share learnings openly, and new hires get real ownership immediately. [abnormal.ai](https://abnormal.ai/about)
- **Notable Perks**: Compensation is tied to impact, not role. The company offers flexibility as infrastructure, not a benefit. Employees see direct results from their work in a fast-growing, mission-driven company.

## Sources
1. [Abnormal AI - Official Website](https://abnormal.ai/)
2. [Abnormal AI - About Page](https://abnormal.ai/about)
3. [Abnormal AI - Careers & Culture](https://abnormal.ai/careers/culture)
4. [Abnormal AI - Open Roles](https://abnormal.ai/careers/open-roles)
5. [Abnormal AI - What We Are](https://abnormal.ai/about/what-we-are)
6. [Crunchbase - Abnormal Security](https://www.crunchbase.com/organization/abnormalsecurity)
7. [PitchBook - Abnormal Security](https://pitchbook.com/profiles/abnormal-security-168693-47)

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