Senior Applied ML Engineer

requirements into scalable technical systems. Make pragmatic build-versus-buy decisions and combine internal models, third-party solutions, and rule-based systems where appropriate. Contribute to the architecture and technical direction of the company’s applied ML platform. What We Are Looking For 5+ years of experience in machine learning, with significant experience deploying ML systems into production. Strong experience with computer vision, multimodal machine learning, content understanding, recommendation, ranking, fraud detection, trust and safety, or a related applied ML domain. Proven ability to independently own complex ML projects from an ambiguous business problem through production launch. Strong understanding of model evaluation, including precision and recall trade-offs, threshold selection, calibration, class imbalance, and cost-sensitive decision-making. Experience building datasets, labeling workflows, evaluation sets, and feedback loops for continuously improving model quality. Experience deploying and operating models at scale, including inference optimization, monitoring, retraining, and incident response. Strong Python skills and experience with modern ML frameworks such as PyTorch. Ability to work with large-scale data and production systems. Strong product judgment and an understanding of how model performance connects to user experience and business outcomes. Ability to communicate technical trade-offs clearly to both technical and non-technical stakeholders. Production Metrics You May Own Precision and recall across different content and policy categories. False-positive rates and the percentage of legitimate user generations incorrectly blocked. False-negative rates and exposure to policy-violating content. User appeal and moderation reversal rates. Generation success and completion rates. Model inference latency and system availability. Cost per classification or generation. Manual review volume and operational workload. Coverage across new models, formats, markets, and policy categories. Impact on user retention, engagement, and conversion.

Nice to Have Experience with trust and safety, content moderation, copyright or intellectual property detection. Experience working with generative image or video models. Experience with vision-language models, embeddings, similarity search, perceptual hashing, or retrieval systems. Experience building human-in-the-loop review and annotation systems. Familiarity with adversarial behavior, model evasion, abuse patterns, and continuously changing content distributions. Experience in a fast-moving startup environment. What Success Looks Like Within your first months, you will independently take ownership of a high-impact applied ML problem, establish a reliable evaluation baseline, deploy an initial production solution, and create a measurable improvement loop based on real user and business outcomes. Over time, you will help build a scalable content intelligence and trust and safety platform that supports new models, products, policies, and markets without creating unnecessary friction for legitimate users. What we offer: Competitive base salary in USD Equity: participation in the company’s stock option program, giving you the opportunity to share in the company’s long-term growth. On-site role in our Almaty office (we will relocate you from anywhere).

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