Machine Learning Engineer

Job Description

We’re looking for a Machine Learning Engineer with a strong foundation in systems engineering and a sharp intuition for model behavior in production. You will work closely with product managers, data engineers, and research scientists to design, deploy, and monitor ML pipelines that directly impact real-time personalization, fraud detection, and search ranking systems. Your work won’t be siloed — we expect deep contributions across training, evaluation, infrastructure, and deployment. We favor engineers who are comfortable building custom training pipelines as much as debugging CUDA memory issues in the middle of a rollout.

Key Responsibilities

• Build and maintain scalable ML pipelines (batch and real-time) using tools like Airflow, Ray, and TensorFlow Extended (TFX)
• Work with large-scale data (100TB+) to generate high-quality features and labels, optimizing both compute efficiency and downstream model performance
• Optimize inference systems for latency and throughput across GPUs and CPUs, including use of TensorRT, ONNX, or custom kernels
• Collaborate with researchers to productionize novel architectures and algorithms with proper fallbacks and monitoring
• Implement continuous evaluation pipelines for model drift, concept drift, and feature skew
• Contribute to architectural decisions around model serving, retraining cadence, and data versioning
• Take ownership of model behavior in production, including alerting, rollbacks, and retraining triggers

Qualifications

Required:

• 4+ years experience building and deploying ML models in production (not just notebooks)
• Strong coding ability in Python and proficiency with core ML libraries (PyTorch or TensorFlow)
• Deep experience with one or more of:
  • Feature engineering on semi-structured logs
  • Model compression and inference optimization
  • Data-centric approaches to improving ML outcomes (e.g., active learning, hard-negative mining)
• Solid grasp of software engineering principles (version control, CI/CD, testing, containerization)

Are you interested in this position?

Apply by clicking on the “Apply Now” button below!

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