About the Role

We're looking for a mid-level Machine Learning Engineer to join our team and help build, deploy, and scale ML solutions that drive real impact. You'll work on the full ML lifecycle—from problem formulation and experimentation to production deployment and monitoring. This role is ideal for someone who has moved beyond the basics and is ready to own projects while continuing to grow their expertise.

What You'll Do

You'll design and implement machine learning models to solve business problems, working closely with data scientists, software engineers, and product teams. Your responsibilities will include building data pipelines, training and evaluating models, deploying them to production environments, and monitoring their performance over time. You'll be responsible for hosting and serving open-source models at scale, optimizing inference performance, and fine-tuning models for specific use cases. You'll contribute to our ML infrastructure, help establish best practices, and mentor junior team members. We expect you to balance moves quickly with building robust, maintainable systems.

What We're Looking For

• experience working with machine learning in production environments
• Strong proficiency in Python and ML frameworks like TensorFlow, PyTorch, or scikit-learn
• Solid understanding of fundamental ML concepts including supervised and unsupervised learning, model evaluation, and feature engineering
• Hands-on experience hosting and serving open-source language models at scale
• Familiarity with inference optimization frameworks like vLLM, TGI (Text Generation Inference), or similar tools
• Experience fine-tuning models using techniques like LoRA, QLoRA, or full fine-tuning, with understanding of the tradeoffs involved
• Proficiency with FastAPI or similar frameworks for building ML APIs
• Strong experience with Docker for containerization and deployment
• Proficiency with data processing tools and cloud platforms (AWS, GCP, or Azure)
• Track record of deploying models to production and handling real-world challenges like latency optimization, throughput management, and model degradation
• Strong communication skills to explain technical concepts to non-technical stakeholders and collaborate effectively across teams

Nice to Have

Experience with Kubernetes for orchestration and scaling, other inference frameworks (TensorRT-LLM, DeepSpeed, Ray Serve), knowledge of model quantization techniques (GPTQ, AWQ, bitsandbytes), familiarity with distributed training frameworks, experience with vector databases and RAG architectures, contributions to open-source ML projects, or experience with MLOps tools and practices would all be valuable additions.