ML Engineer
Role Overview
This is a senior-level Machine Learning Engineer role focused on fine-tuning and deploying Large Language Models (LLMs) for tasks like natural language understanding and text generation. You'll work autonomously in a small, technical, product-focused team to build and optimize AI-driven systems that enhance user interactions with expert insights. Responsibilities include designing models, optimizing performance and cost, maintaining data pipelines, and integrating features into production.
Perks & Benefits
The role offers fully remote work with a flexible schedule, likely accommodating various time zones, and involves direct collaboration with a lean, high-impact team passionate about AI. You'll have the opportunity to influence the company's AI roadmap and work on transforming access to human expertise, with an emphasis on product quality and technical innovation.
Full Job Description
Machine Learning Engineer (LLM Fine-Tuning) | Remote
Location: Fully Remote
Contract Rate: Up to USD78/hour
About our Client
Our client helps business leaders make better strategic decisions through in-depth expert interviews and curated insights. Our mission is to transform the way executives access and apply real-world expertise.
We’re a small, highly technical, and product-focused team working to leverage AI to scale human expertise.
About the Role
We’re seeking a Machine Learning Engineer experienced in fine-tuning and deploying Large Language Models (LLMs). You’ll work closely with our product and data teams to build, refine, and operationalize intelligent systems that enhance how our users interact with expert insights.
This is a hands-on engineering role, ideal for someone who’s comfortable working autonomously and thrives in a fast-moving environment.
Responsibilities
Design, fine-tune, and deploy LLMs for natural language understanding, text generation, and summarization tasks.
Optimize existing ML models for performance, cost, and latency.
Build and maintain robust data pipelines for model training and evaluation.
Collaborate with cross-functional teams to integrate AI-driven features into production systems.
Continuously explore new techniques in prompt engineering, retrieval-augmented generation (RAG), and model optimization.
Requirements
Proven experience fine-tuning and deploying LLMs (OpenAI, Anthropic, Mistral, LLaMA, etc.).
Strong background in machine learning engineering, with experience in Python and frameworks such as PyTorch, TensorFlow, or Transformers.
Solid understanding of NLP, model evaluation, and data preprocessing.
Experience building end-to-end ML systems, from data ingestion to deployment.
Familiarity with MLOps tools and cloud infrastructure (AWS, GCP, or Azure).
Excellent communication and documentation skills.
Nice to Have
Experience working with vector databases (Pinecone, Weaviate, FAISS).
Understanding of RAG, prompt tuning, or instruction fine-tuning.
Previous work in content intelligence, research, or knowledge management platforms.
Why Join
Work directly with a lean, high-impact team passionate about AI and product quality.
Fully remote and flexible working schedule.
Opportunity to influence the AI roadmap of a company transforming access to human expertise.
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