Job Description

Machine Learning Engineer

Fully Remote

$170,000-$200,000 base + bonus + equity

About the Company

Our client is building technology focused on making AI more secure and safe for the future of humanity. They are tackling critical challenges at the intersection of AI, security, and infrastructure, with a mission-driven approach and an emphasis on high standards, speed, and technical excellence.

About the Team

This is a small, world-class team taking on difficult problems across AI, security, and infrastructure. They move quickly, take ownership of everything they ship, and operate with a high degree of accountability. Remote work is treated as a tool for focus and execution. The environment is intense, highly collaborative, and built for people who want to push beyond standard expectations to help build technology that could redefine how the world trusts AI.

What You’ll Do

• Design and build machine learning systems that analyze, classify, and extract insights from large-scale, multimodal data
• Develop, fine-tune, and evaluate transformer and LLM-based models for language understanding, generation, and evaluation
• Prototype quickly, experiment relentlessly, and own models end-to-end
• Engineer high-performance inference pipelines optimized for latency, throughput, and accuracy
• Work with LLMs and agents to model language, actions, and context across complex workflows

Qualifications

• 3-6 years of hands-on machine learning or applied AI experience
• Strong Python and PyTorch experience, or strong Python and TensorFlow experience
• Deep understanding of transformer architectures and NLP pipelines
• Experience training, fine-tuning, or evaluating LLMs and agentic systems for applied NLP tasks
• Comfortable working with REST and/or gRPC APIs, Git, and cloud ML stacks across AWS, GCP, or Azure

Nice to Have

• Experience with LoRA, PEFT, RAG, or vector search
• Experience with instruction tuning, evaluation datasets, or alignment methods
• Knowledge of GPU optimization or distributed training
• Interest in AI safety, interpretability, and bias evaluation
• Open-source contributions or published machine learning work

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