Machine Learning Engineer

Job Description

We’re looking for a Machine Learning Engineer who thrives on building scalable ML systems that move beyond prototypes. You’ll work on productionizing advanced models, optimizing data pipelines, and designing model deployment strategies that support low-latency, high-availability applications. Our current initiatives focus on deep learning models for recommendation systems, real-time decision engines, and offline batch learning pipelines with billions of records.

This role requires someone who can bridge research and engineering: comfortable diving into novel model architectures while understanding the constraints of distributed systems. You’ll work cross-functionally with data scientists, product engineers, and platform teams to deliver intelligent systems that operate at scale and evolve over time.

Key Responsibilities

• Design, build, and maintain production-ready ML pipelines (ETL, training, validation, deployment, monitoring).
• Own model lifecycle management—from prototyping to A/B testing, rollout, and continuous improvement.
• Collaborate with data scientists to optimize model architectures for scalability and inference performance.
• Implement MLOps best practices including versioning (model/data/code), reproducibility, and CI/CD for ML.
• Work with platform engineers to deploy models into distributed systems, including real-time and batch environments.
• Profile and optimize models for inference efficiency, memory footprint, and latency constraints.
• Contribute to the design of automated systems for concept drift detection, data quality checks, and model retraining.
• Translate ambiguous business requirements into measurable ML objectives and deployment strategies.

Qualifications

Required:

• 3–6 years of experience building and deploying machine learning models in production environments.
• Proficiency in Python and ML libraries such as PyTorch, TensorFlow, or JAX.
• Strong knowledge of data processing frameworks like Apache Beam, Spark, or Flink.
• Experience with model serving frameworks (e.g., TorchServe, TensorFlow Serving, Triton, or custom REST gRPC APIs).
• Proven ability to implement and tune deep learning architectures (e.g., transformer models, CNNs, RNNs).
• Deep understanding of feature engineering, data labeling workflows, and data versioning tools (e.g., Feast, DVC).
• Experience with Docker, Kubernetes, or similar container orchestration for ML deployment.
• Familiarity with model evaluation metrics across classification, ranking, and regression tasks.
• BS or MS in Computer Science, Electrical Engineering, or related field.

Preferred:

• Experience deploying models at scale (tens of millions of users, low-latency applications).
• Exposure to online learning or reinforcement learning systems.
• Experience building internal ML tools or platform components (feature stores, training orchestration, monitoring dashboards).
• Understanding of regulatory constraints on model fairness, privacy, or interpretability in production.
• Contributions to open-source ML tooling or infrastructure.

Are you interested in this position?

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

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