Machine Learning Engineer

ABOUT THIS FEATURED OPPORTUNITY

Play a part in the next revolution in human-computer interaction. Build groundbreaking technology for large-scale systems, spoken language, big data, and artificial intelligence.

The AI/ML – Machine Translation team is seeking exceptional Machine Learning Engineers who are passionate about enhancing customer experience and building robust ML automation tooling, with a strong emphasis on scalable model automation pipelines and production deployment.

This is an infrastructure-focused role centered on building and automating end-to-end machine learning pipelines — not conducting research or fine-tuning models.

The primary responsibility is to design and implement a scalable, fully automated ML platform that supports the entire lifecycle:

• Model building
• Training
• Evaluation
• Production deployment

The ideal candidate has a strong understanding of the ML lifecycle and hands‑on experience with model development, but their primary focus will be automation, efficiency, and platform scalability.

You will:

• Optimize model training and evaluation workflows
• Improve scalability, including extending workloads across multiple GPUs
• Build systems that operate across fleets of machines
• Leverage distributed computing to process large-scale data in parallel
• Ensure systems are fast, reliable, and production-ready
• Identify and resolve pain points in model development workflows

This role enables researchers to prototype efficiently by building the distributed technical stack and ML infrastructure that powers their work.

Note: No research or model fine‑tuning responsibilities are expected. This is a platform and infrastructure engineering role supporting ML researchers.

• 3+ years of hands‑on experience in ML infrastructure, platform engineering, or ML lifecycle management
• Deep understanding of the machine learning lifecycle
• Proven ability to design, build, and maintain end‑to‑end automated ML pipelines spanning training, evaluation, and production deployment
• Experience operationalizing models into reliable, scalable production systems

• Demonstrated experience designing and operating distributed computing systems across multiple machines
• Experience supporting multi‑GPU training and large‑scale parallel data processing
• Strong focus on scalability, performance optimization, and reducing training time through efficient system design

• Hands‑on experience deploying and managing ML systems in at least one major cloud environment (AWS, GCP, or Azure)
• Experience building cloud‑based infrastructure that supports scalable, production‑ready ML workflows

• Strong programming skills in Python
• Ability to write clean, modular, and testable code
• Demonstrated engineering rigor including:
  • Unit testing
  • CI/CD implementation
  • Structured code reviews
  • High standards for reliability, maintainability, and documentation

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

• Experience with Large Language Models (LLMs) or Neural Machine Translation
• Deep knowledge of ML frameworks and technologies such as NLP, Machine Translation (MT), ASR, PyTorch, Tensor Flow, JAX, and transformer architectures

Technology is our focus and quality is our commitment. As a national expert in delivering flexible technology and talent solutions, we strategically align industry and technical expertise with our clients’ business objectives and cultural needs. Our solutions are tailored to each client and include a wide variety of professional services, project, and talent solutions. By always striving for excellence and focusing on the human aspect of our business, we work seamlessly with our talent and clients to match the right solutions to the right opportunities.

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