Senior Machine Learning Engineer

What you'd actually do

1. Architect and own end-to-end ML infrastructure for training, deployment, and serving across batch and real-time environments.
2. Define and drive technical standards for ML systems, including reliability, observability, and performance benchmarks.
3. Lead the design and implementation of scalable data pipelines for large-scale feature engineering and model training.
4. Own system performance and reliability across the ML platform, proactively identifying and resolving bottlenecks.
5. Establish and evolve ML Ops practices, including CI/CD pipelines, monitoring, alerting, and A/B testing frameworks.

Skills

What the JD emphasized

• track record of delivering production ML systems
• deep, proven experience in at least two of the following: software engineering, data engineering, distributed systems, or applied machine learning
• deep proficiency in Python
• hands-on experience with ML frameworks like TensorFlow or PyTorch
• deep understanding of ML algorithms, model architectures, and the infrastructure required to build scalable, reliable ML systems
• proficient with cloud platforms (e.g., AWS, GCP), containerization (Docker, Kubernetes), and distributed data systems (e.g., Spark, Kafka)
• proven experience with ML model serving technologies (e.g., MLflow, TensorFlow Serving), CI/CD tooling, and ML platform design

Other signals

• ML infrastructure
• ML Ops
• production ML systems
• ranking problems
• recommendation engines
• pricing optimization