Senior Machine Learning Engineer (AI Decisioning)

What You Will Do

• 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.

What You Will Need

You don’t need to tick every box

if you likely have

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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

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Familiarity with sequential recommendation, ranking, or joint / multi-objective optimization problems where you can't optimize one metric without trading off another

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A/B tested the impact you’ve provided and iterated based on what the data said

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Comfortable with the messy reality of production ML: cold start, sparse signal, label delay, feedback loops, distribution shift

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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

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Software engineering, production-quality code and at least one programming language, care about API contracts, testing, and observability, not just notebooks

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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

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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

and it would be a strong plus if you have any of below

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Hands-on experience with online decision-making under uncertainty, multi-armed bandits, contextual bandits, Thompson sampling, UCB, or RL agents that have served traffic

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Comfortable reasoning about exploration vs exploitation, regret, off-policy evaluation (IPS, doubly robust), counterfactual estimation, and the failure modes of each

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Causal inference / uplift modeling

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Academic research experience or publications in online decision-making under uncertainty, reinforcement learning, sequential recommendation, optimization, or related fields.