We are seeking a Senior Machine Learning Engineer to design, build, and scale production-grade ML systems across multiple domains (NLP, text, audio, images, video).
You’ll play a pivotal role in bridging research with engineering, ensuring our AI features are robust, efficient, and deployed at scale.
Key Responsibilities:
• Build Production ML Systems: Design, build, and maintain scalable infrastructure for training, evaluation, and real-time inference across multiple modalities.
• Optimize for Performance: Profile, debug, and optimize ML models for latency, throughput, and cost, using techniques such as quantization, distillation, and efficient resource allocation.
• Bridge R&D and Engineering: Act as the technical link between R&D and engineering teams, driving best practices in software engineering and system design for ML.
Mandatory Skills:
• 5+ years of experience as a Machine Learning Engineer, Software Engineer, or DevOps/SRE with strong focus on ML systems.
• Expert-level Python skills for ML applications (FastAPI, Flask, etc.).
• Strong proficiency in at least one OOP language: C#, C++, or Java.
• Production experience with ML frameworks: PyTorch, TensorFlow, and serving tools (TorchServe, TensorFlow Serving, TensorRT, Triton, OpenVINO).
• Solid foundation in ML theory (regression, classification, clustering, dimensionality reduction) and DL architectures (CNNs, RNNs, Transformers, Diffusion, Generative AI).
• Experience building and managing ML systems on Azure, AWS, or GCP.
• Strong debugging skills for complex distributed systems.
• Proactive, problem-solving mindset with passion for robust, elegant solutions.
• Excellent communication and collaboration skills; fluent in English.
• Curiosity and eagerness to learn continuously.
Nice to Have
• Familiarity with Agile (Scrum).
• Experience with data engineering tools and orchestrators (Spark, Airflow, Kubeflow Pipelines).
• Hands-on with Docker, Kubernetes, and Infrastructure as Code (Terraform).
• Experience building CI/CD pipelines (Azure DevOps, GitHub Actions, Jenkins).
• Knowledge of low-latency model inference (ONNX, TensorRT, OpenVINO, quantization).
• Exposure to low-level ML programming (CUDA, OpenCL, ROCm).
• Experience with cloud ML platforms (Azure AI Foundry, Vertex AI, SageMaker).
• Experience with distributed computing frameworks (Apache Spark).
• Contributions to open-source, public portfolio (GitHub, Kaggle), publications, or conference talks.
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