Senior Machine Learning Engineer (AI Decisioning)
Work from anywhere in Turkey through our fully remote setup.Full-TimeSenior
Salary not disclosed
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Job Details
- Required Skills
- AWSArtificial IntelligenceMachine LearningSoftware EngineeringA/B testing
Requirements
- Designed and deployed personalization, ranking, or recommendation systems used by real users; improved core engagement metrics (CTR, conversion, retention, revenue) and can talk concretely about the lift
- Familiarity with sequential recommendation, ranking, or joint / multi-objective optimization problems where you can't optimize one metric without trading off another
- A/B tested the impact you’ve provided and iterated based on what the data said
- Comfortable with the messy reality of production ML: cold start, sparse signal, label delay, feedback loops, distribution shift
- Solid grounding in probabilistic modelling (Bayesian inference, calibration, hierarchical models) and modern recommender techniques (embeddings, sequence models, LLM-driven content understanding), applied to sequential, ranking, or multi-objective problems
- Software engineering, production-quality code and at least one programming language, care about API contracts, testing, and observability, not just notebooks
- Built high-throughput real-time or batch pipelines supporting ML training and inference, on AWS (or an equivalent major cloud) comfortable owning a service end to end across compute, storage, networking, and CI/CD
- Have moved at least one model from a paper, a notebook, or a whiteboard sketch into a real system that serves traffic, and can speak honestly about what broke along the way
Responsibilities
- Design, build and release real-time decisioning systems that learn from interaction feedback at scale.
- Own the modeling logic from how we represent users and signals to how reward attribution closes the loop and improves the next interaction.
- Move ideas from papers and notebook prototypes including online learning policies and counterfactual estimators to production code that runs behind real-time APIs.
- Design and build resilient streaming/batch pipelines that feed user state, reward signals, and offline replay.
- Run honest experiments, A/B test what you ship, design offline evaluation for what you cannot, and kill your own work. when it doesn’t outperform against the baseline.
- Continuously monitor and improve the quality, latency, observability, and scalability of the systems.
- Collaborate across platform and product teams to turn research-grade ideas into production-grade products.
- Share context, mentor engineers, raise the technical bar of the team, and help set direction for how we do ML at scale.
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