Machine Learning Engineer

Machine Learning EngineerFully Remote$170,000-$200,000 base + bonus + equityAbout the CompanyOur 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 TeamThis 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 DoDesign and build machine learning systems that analyze, classify, and extract insights from large-scale, multimodal dataDevelop, fine-tune, and evaluate transformer and LLM-based models for language understanding, generation, and evaluationPrototype quickly, experiment relentlessly, and own models end-to-endEngineer high-performance inference pipelines optimized for latency, throughput, and accuracyWork with LLMs and agents to model language, actions, and context across complex workflowsQualifications3-6 years of hands-on machine learning or applied AI experienceStrong Python and PyTorch experience, or strong Python and TensorFlow experienceDeep understanding of transformer architectures and NLP pipelinesExperience training, fine-tuning, or evaluating LLMs and agentic systems for applied NLP tasksComfortable working with REST and/or gRPC APIs, Git, and cloud ML stacks across AWS, GCP, or AzureNice to HaveExperience with LoRA, PEFT, RAG, or vector searchExperience with instruction tuning, evaluation datasets, or alignment methodsKnowledge of GPU optimization or distributed trainingInterest in AI safety, interpretability, and bias evaluationOpen-source contributions or published machine learning work