ABOUT THIS FEATURED OPPORTUNITY

Join the AI/ML – Machine Translation team to build scalable ML infrastructure powering large-scale AI systems. We are seeking a Machine Learning Engineer with mid-level ML lifecycle experience and strong distributed data processingskills to design and operate platforms supporting model development from training to production.

OPPORTUNITY FOR YOU

This infrastructure-focused role centers on owning and optimizing mid-level ML lifecycle stages, not research or model fine-tuning. You will build automated ML systems for:

• Data ingestion and preprocessing at scale

• Distributed model training

• Evaluation and deployment workflows

• Monitoring and iteration post-deployment

You will:

• Design and optimize distributed data pipelines for large-scale datasets

• Improve multi-GPU and multi-node training workflows

• Automate ML lifecycle processes to reduce manual effort

• Identify bottlenecks in data processing, training, and deployment

• Ensure systems are scalable, reliable, and production-ready

This role enables researchers to prototype efficiently by building robust distributed infrastructure.

KEY SUCCESS FACTORS

ML Lifecycle & Automation

• 3+ years supporting mid-level ML lifecycle stages: data prep, training orchestration, evaluation, deployment, monitoring

• Experience moving models from development to production

• Knowledge of experiment tracking, model versioning, and CI/CD for ML workflows

• Ability to build automated end-to-end ML pipelines

Distributed Systems & Scalability

• Experience with distributed computing systems (Spark, Ray, Dask, etc.)

• Multi-GPU and multi-node training management

• Proven ability to optimize training throughput and large-scale data pipelines

Cloud & Production

• Experience with cloud-based scalable ML platforms (AWS, GCP, Azure)

• Familiarity with containerization and orchestration (Docker, Kubernetes)

• Strong Python skills and CI/CD implementation for ML workflows

Education

• B.S. or M.S. in Computer Science, Machine Learning, Statistics, or related field

NICE TO HAVE

• Experience with LLMs or Neural Machine Translation

• Familiarity with NLP, ASR, PyTorch, TensorFlow, JAX

• Knowledge of transformer architectures and large-scale training environments

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