Machine Learning Engineer (Ads Ranking)

Location: Remote from multiple eligible countries, including Canada, Ireland, and the United States. Availability is required for coordination hours from 9am to 3pm Pacific Time, Monday through Friday.

Salary: US-based applicants: $189,507–$274,604 USD annually, plus equity and benefits. Canadian applicants: CAD $227,108–$263,271 annually outside Toronto and Vancouver, or CAD $243,330–$282,076 annually in Toronto and Vancouver.

Company

hirify.global operates global knowledge-sharing and AI language-model platforms, including hirify.global and Poe.

What you will do

• Develop and improve advertising ranking models for CTR and CVR prediction, feature interactions, user-history modeling, and calibration.
• Own machine learning systems end to end, including data pipelines, feature engineering, training-data construction, evaluation, training, deployment, and maintenance.
• Evaluate and apply deep learning and recommendation-modeling advances within production latency, reliability, and cost constraints.
• Collaborate with ML platform, product, data science, and engineering teams to build scalable production systems.
• Design A/B experiments and measure the impact of ranking improvements on advertiser performance, revenue, and user relevance.
• Identify additional opportunities to apply machine learning across the advertising product.

Requirements

• Must be available for coordination hours from 9am to 3pm Pacific Time, Monday through Friday.
• 4+ years of professional software development experience in machine learning.
• Hands-on experience developing and deploying large-scale ads ranking models, including CTR or CVR prediction and calibration.
• Experience evaluating ranking models through offline analysis and online experiments, including analysis of discrepancies between model metrics and business outcomes.
• Experience building and deploying deep learning models with PyTorch or TensorFlow, with strong Python skills and maintainable production ML code.
• BS, MS, or PhD in Computer Science, Engineering, or a related technical field.

Nice to have

• Experience with feature interaction networks, attention-based user-sequence models, multi-task learning, or generative recommender systems.
• Understanding of ranking calibration, bidding, auctions, and ad delivery outcomes.
• Experience addressing sparse or delayed conversion labels, sampling and exposure bias, cold-start users, or training-serving inconsistencies.
• Experience leading large-scale projects involving multiple engineers.
• Experience using AI-assisted development tools with sound judgment in validating generated code and conclusions.

Culture & Benefits

• Remote-first work with flexibility and autonomy.
• Transparent, collaborative culture focused on experimentation, learning, and idea-sharing.
• Medical, dental, and vision coverage, with benefits varying by country.
• Equity refreshers, remote work reimbursement, paid time off, and employee assistance programs.
• Final candidates undergo identity verification and a comprehensive background check before onboarding.

Hiring process

• Applications are accepted on an ongoing basis.
• AI technology may assist with application sorting and interview-note recording, while hiring decisions are made by team members.