ML Engineer (LLMs)

Description

LanguageWire is a global leader in language and content services, dedicated to helping brands achieve their full global potential. Since 2000, we’ve evolved and built unique technology that enables seamless multilingual communication. Our worldwide network of language experts, together with our intuitive online platform, empowers brands to translate and manage multilingual content effortlessly.

The role you’ll play

You will help improve our existing ML-based products and build new features and services using LLMs. A core part of the role is context engineering — designing the right inputs, retrieval strategies, and prompt structures to get the best possible output from LLMs in production. You'll also contribute to our software engineering efforts when needed, bridging the gap between ML and software engineering on the team.

The team you’ll be a part of

You will work in the Machine Learning Team, where we build LanguageWire's machine translation (MT) and adjacent services to monitor and predict its performance. The team consists of ML Engineers, Analytics Engineers, and Full Stack Software Engineers. You'll collaborate across these disciplines daily, and we value people who can move fluidly between ML experimentation and production engineering. You'll report to the team leader.

If you want to make a difference, make it with us by…

• Context engineering LLM-based translation workflows — designing retrieval strategies, prompt structures, and input pipelines that maximize output quality.
• Productionizing the prototypes that prove their value.
• Staying current with LLM research & tools, bringing relevant findings back to the team.
• Constructing and evaluating experiments to measure what actually works.

In one year, you’ll know you were successful if…

• We have made meaningful progress toward agentic MT — where translation workflows can reason, self-correct, and orchestrate multiple steps autonomously.
• Our LLMOps practices are mature enough to deploy, monitor, and iterate on LLM-based services with confidence.
• Our ways to evaluate MT have improved, applying techniques such as LLM-as-a-judge.
• We can offer even better personalized MT for our customers using LLMs.

Desired experience and competencies

What you’ll need to bring

• 2–3+ years of relevant experience from areas such as ML engineering, AI engineering, or software engineering with a strong ML focus.
• Hands-on experience with LLMs and prompt/context engineering — either professional or through substantial personal projects.
• Experience with best practices and building production grade systems in Python. Knowledge of other programming languages is considered a bonus.
• Experience constructing and evaluating experiments.
• A strong analytical mindset — you think in terms of hypotheses, baselines, and measurable outcomes.
• A keen interest in the rapidly evolving LLM space and tooling.

This will make you stand out

Experience with:

• Context engineering — designing retrieval, chunking, and prompt strategies for production LLM systems.
• Building agentic products or prototypes.
• Training or fine-tuning ML models.
• LLMOps — deployment, monitoring, evaluation, and iteration of LLM-based services.
• Building production-grade, scalable software systems.