Your opportunity
As a crucial member of our team, you'll play a pivotal role across the entire machine learning lifecycle, contributing to our conversational AI bots, RAG system and traditional ML problem solving for our observability platform.  Your tasks will encompass both operational and engineering aspects, including building production-ready inference pipelines, deploying and versioning models, and implementing continuous validation processes. On the LLM side you'll fine-tune generative AI models, design agentic language chains, and prototype recommender system experiments.
What you'll do
  • Fine-tuning generative AI models to enhance performance.
  • Designing AI Agents for conversational AI applications.
  • Experimenting with new techniques to develop models for observability use cases
  • Building and maintaining inference pipelines for efficient model deployment.
  • Managing deployment and model versioning pipelines for seamless updates.
  • Developing tooling to continuously validate models in production environments.

This role requires
  • 2+ Years Demonstrated proficiency in software engineering design practices.
  • Bachelor's or advanced degree in Computer Science, Engineering, Mathematics, or a related field. Advanced degree (Master's or Ph.D.) preferred.
  • Experience working with transformer models and text embeddings.
  • Proven track record of deploying and managing ML models in production environments.
  • Familiarity with common ML/NLP libraries such as PyTorch, Tensorflow, HuggingFace Transformers, and SpaCy.
  • preferred experience developing production-grade applications in Python.
  • Proficiency in Kubernetes and containers.
  • Familiarity with concepts/libraries such as sklearn, kubeflow, argo, and seldon.
  • Expertise in Python, C++, Kotlin, or similar programming languages.
  • Experience designing, developing, and testing scalable distributed systems.
  • Familiarity with message broker systems (e.g., Kafka, RabbitMQ).
  • Knowledge of application instrumentation and monitoring practices.
  • Experience with ML workflow management, like AirFlow, Sagemaker, etc.
  • Bonus: Familiarity with the AWS ecosystem.
  • Bonus: Past projects involving the construction of agentic language chains.


Bonus points if you have
  • Experience in LangChain 

Is a Remote Job?
Hybrid (Remote with required office time)

New Relic helps engineers and developers do their best work every day — using data, not opinions — at every stage of the software lifecycle. The world’s best engineering teams rely on New Relic to...

Apply Now