AI Engineer
Location:
Remote
Salary:
$140K–$160K
About the Role
You will design build and optimize the machine learning and rules based systems that power dynamic variables creative generation localization intelligence and real time decision logic. You transform raw product catalog usage and performance data into reliable scalable models and services that drive automation accuracy and measurable lift. You partner closely with data engineering backend frontend product and design for fast iteration and safe deployment.
Core Responsibilities
Own model and rules design for tasks such as text variant suggestion image asset selection language detection audience segmentation and performance prediction
Build data pipelines that ingest clean enrich and serve structured data sets for training validation and inference with clear provenance
Implement feature engineering strategies including extraction normalization embedding generation and drift monitoring
Train evaluate and tune models using reproducible experiments with transparent metrics precision recall lift latency and cost
Deploy inference services with attention to throughput latency resource usage scaling strategy and graceful degradation paths
Instrument monitoring alerting and automated rollback for model health data quality concept drift and infrastructure issues
Collaborate with product to define success metrics and with engineering to integrate inference endpoints into user facing workflows
Document model assumptions data schema feature definitions evaluation methodology and operational playbooks
Ensure responsible AI practices including bias checks privacy safeguards rate limiting and audit logging
Contribute to architectural decisions technical roadmaps and incremental delivery plans
You Will Succeed If You Have
Four or more years building production ML or AI systems in a SaaS or platform context
Strong proficiency in Python plus experience with libraries such as PyTorch TensorFlow scikit learn or similar
Solid knowledge of data pipeline tooling job orchestration and storage patterns
Experience deploying models behind APIs with attention to performance observability and reliability
Ability to translate ambiguous product goals into concrete model tasks evaluation plans and iterative milestones
Good command of statistics experiment design and error analysis
Clear written and verbal communication with focus on concise documentation and collaborative problem solving
Nice To Have
Experience with natural language tasks related to ad copy generation variant ranking or multilingual processing
Familiarity with recommendation systems reinforcement learning bandit algorithms or causal inference for marketing performance
Exposure to image processing or lightweight computer vision for asset selection or quality scoring
Knowledge of vector databases semantic search or embedding based retrieval architectures
Basic SQL competence for exploratory analysis and feature validation
What We Offer
Ownership of high impact AI surfaces central to product differentiation
Access to rich real world data sets product usage logs creative performance metrics and localization contexts
Environment that values fast safe experimentation with clear rollback and monitoring
Learning budget for courses conferences books and professional coaching
Collaborative culture focused on openness continuous improvement measurable outcomes and ethical responsibility