A globally recognised technology company — operating through a partner hiring platform — is seeking an experienced Engineering Manager, Identification Accuracy for its UAE-based remote team. This high-impact engineering leadership role sits at the intersection of machine learning, data science, and fraud prevention — leading a multidisciplinary team responsible for improving the accuracy and reliability of a critical identification platform that operates at billions-of-devices scale. Combining people leadership, technical strategy, and programme direction, this fully remote role offers compensation benchmarked at $159,000–$215,000 USD with high autonomy and meaningful influence over ML strategy, team development, and product outcomes.
About This Engineering Manager — Identification Accuracy Opportunity
Scale: Production ML systems operating across billions of devices — one of the largest and most technically demanding identification accuracy challenges in the global technology industry
Team: Multidisciplinary Identification Accuracy team spanning ML engineers, data scientists, analysts, and analytics engineers — led by this Engineering Manager
Domain: Fraud detection, identity, trust & safety — applying ML to protect enterprises and high-growth companies from identification-based fraud at massive scale
Model: Fully remote, globally distributed team — with high autonomy, psychological safety culture, and a genuine commitment to technical excellence and continuous improvement
Why This Engineering Manager Role Stands Out in UAE 2026
Billions-of-Devices Scale: Lead the development of ML models that improve identification performance across billions of devices — a genuinely rare scale of engineering leadership challenge
Full ML Lifecycle Ownership: Data pipelines, feature engineering, model training, evaluation, deployment, MLOps, and operational processes — end-to-end technical strategy and delivery authority
Premium Remote Compensation: $159K–$215K USD benchmark — among the highest-paying remote Engineering Manager positions available to UAE-based ML leaders in 2026
High Autonomy & Strategic Influence: Shape the team roadmap, guide production ML system development, and influence both technology and people strategy at the intersection of fraud detection and identity
Position Overview
This Engineering Manager — Identification Accuracy in UAE leads and grows a multidisciplinary team of ML engineers, data scientists, analysts, and analytics engineers — fostering psychological safety, technical excellence, and continuous improvement. The role owns the team’s technical roadmap in collaboration with senior engineering leadership, drives measurable model accuracy outcomes by enabling the team to design, train, evaluate, and deploy ML models that improve identification performance across billions of devices, oversees production ML systems across data pipelines, feature engineering, model development, evaluation, and deployment, partners with platform and API engineering teams on downstream requirements and latency constraints, collaborates with Product and customer-facing teams to translate business priorities into technical initiatives, and builds a high-performing organisation by mentoring team members and developing technical leaders.
Why This Role Matters: As Engineering Manager — Identification Accuracy working remotely from the UAE, you lead the team that determines whether a critical identification platform can distinguish a legitimate user from a fraudster across billions of device interactions every day — at a false positive rate low enough to avoid frustrating genuine customers and a false negative rate low enough to protect enterprises from fraud losses. Every model accuracy improvement your team ships, every data quality issue they resolve, and every MLOps process they improve directly changes the commercial and security outcomes for the major enterprises and high-growth companies that depend on this platform. This is engineering leadership where the stakes are measurable, the scale is extraordinary, and the impact is real.
Key Accountabilities
Team Leadership, People Development & Culture Building
- Lead and grow a multidisciplinary Identification Accuracy team spanning ML engineers, data scientists, analysts, and analytics engineers — fostering psychological safety, technical excellence, accountability, and continuous improvement
- Build a high-performing organisation by mentoring team members, developing emerging technical leaders, and creating an environment where people can consistently do their best work
- Coach and develop individual contributors toward greater autonomy, technical depth, and cross-functional effectiveness — building the team capability that sustains long-term model accuracy improvement
Technical Roadmap, Model Accuracy & Production ML Systems
- Own the team’s technical roadmap in collaboration with senior engineering leadership and cross-functional stakeholders — identifying opportunities to improve model quality and address complex identification challenges
- Drive measurable model accuracy outcomes by enabling the team to design, train, evaluate, and deploy machine learning models that improve identification performance across billions of devices
- Oversee the delivery of production ML systems across data pipelines, feature engineering, model development, evaluation, and deployment — ensuring reliability, scalability, and operational excellence at massive scale
- Continuously improve engineering and ML practices including experimentation methodology, model evaluation frameworks, MLOps tooling, data workflows, and operational processes
Cross-Functional Collaboration & Stakeholder Communication
- Partner closely with platform and API engineering teams to understand downstream requirements, performance expectations, and latency constraints — ensuring ML systems meet the technical needs of dependent services
- Collaborate with Product and customer-facing teams to translate customer needs and business priorities into concrete technical initiatives and product improvements
- Communicate model performance, data-quality considerations, technical trade-offs, risks, and roadmap priorities clearly to both technical teams and senior business stakeholders
- Work with large-scale behavioural or event data in production environments — applying analytics engineering tools including dbt to maintain data quality and pipeline reliability
Requirements & Qualifications
Essential Requirements
- 5+ years of professional experience in software engineering, machine learning, data science, or a closely related technical discipline — including at least 2 years leading an ML or data science team in a fast-paced production environment
- Proven experience managing technical teams that deliver production machine learning systems — from data pipelines and feature engineering through model training, evaluation, and deployment
- Demonstrated success building and developing high-performing multidisciplinary teams including ML engineers, data scientists, analysts, or analytics engineers
- Strong technical understanding of machine learning and data systems — with familiarity with MLOps practices and tooling including experiment tracking, feature stores, model registries, and ML CI/CD pipelines
- Experience working with large-scale behavioural or event data in production environments
- Hands-on familiarity with data stack and analytics engineering technologies such as dbt or similar tools
- Excellent written and verbal communication skills — ability to translate complex model behaviour, data quality challenges, and technical trade-offs for both technical and non-technical audiences
- Strong people leadership skills including coaching, mentoring, team development, and fostering psychological safety and high performance
- Demonstrated ability to deliver results in rapidly scaling environments where priorities evolve and ambiguity is part of the work
Preferred Experience
- Experience in fraud detection, identity, trust & safety, or a related domain — a plus but not required
- Background in identification accuracy, device intelligence, or behavioural analytics systems at scale
About This Role — ML Leadership at Billions-of-Devices Scale
This Engineering Manager — Identification Accuracy role represents a rare opportunity to lead ML model development and team building at a scale that few engineering managers encounter — identification systems operating across billions of devices, generating the behavioural and event data that a multidisciplinary team of ML engineers, data scientists, and analytics engineers transforms into the model accuracy improvements that separate reliable fraud detection from the alternative. Offered fully remote with UAE authorisation and competitive compensation benchmarked against the US market, this role gives experienced ML engineering managers in the UAE the opportunity to build high-performing teams, shape a meaningful technical roadmap, and drive outcomes that protect major enterprises and high-growth companies from identification-based fraud on a global scale.
Career Excellence: Lead identification accuracy ML at billions-of-devices scale — fraud detection, MLOps, team development, $159K–$215K, fully remote from UAE
Who Should Apply?
- Senior ML Engineering Managers: With 5+ years of ML/data science experience and 2+ years leading multidisciplinary ML teams — delivering production models from pipeline to deployment at significant scale
- Fraud Detection & Identity ML Leaders: With domain experience in fraud prevention, identity systems, trust & safety, or device intelligence — leading teams that apply ML to high-stakes accuracy challenges
- MLOps-Proficient Technical Managers: With hands-on familiarity with experiment tracking, feature stores, model registries, and ML CI/CD pipelines — able to improve team practices as well as direct delivery
- Remote-First Engineering Leaders: Comfortable leading globally distributed, fully remote multidisciplinary teams — with strong async communication skills and a high-trust management approach
- UAE-Based ML Leaders — $159K-$215K Benchmark: Seeking a premium remote engineering leadership position that combines technical depth, people development, and strategic influence over a globally significant ML platform
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