ML Engineer | $120/hr | Remote

Machine Learning Engineers, ML Systems & Inference Experts. We are seeking experienced Machine Learning Engineers, ML Researchers, AI Engineers, ML Systems Engineers, Inference Engineers, and Performance Optimization Specialists to contribute to a technically challenging AI training project. What You’ll Do Develop and validate machine learning models and componentsBuild reproducible training and inference workflowsImplement model architectures, tensor operations, data pipelines, tokenization, batching, and generation systemsReview and debug AI-generated machine learning codeDiagnose numerical instability, incorrect tensor behavior, memory issues, and performance regressionsOptimize inference and training for latency, throughput, memory consumption, and hardware utilizationCompare multiple model implementations for correctness, efficiency, and reproducibilityBuild benchmarks and objective evaluation criteriaDevelop tests that verify model behavior and performanceDebug distributed and system-level ML failuresDocument technical decisions, engineering tradeoffs, assumptions, and limitationsCreate reference implementations and technically rigorous solutions Who Can Apply Relevant backgrounds include: Machine Learning Engineers, Senior Machine Learning Engineers, Staff Machine Learning Engineers, Principal Machine Learning Engineers, Lead Machine Learning Engineers, Applied Machine Learning Engineers, and ML Software Engineers. We also welcome: AI Engineers, Senior AI Engineers, Artificial Intelligence Engineers, Applied AI Engineers, Generative AI Engineers, Deep Learning Engineers, Neural Network Engineers, and AI Systems Engineers. Research backgrounds may include: Machine Learning Researchers, Research Engineers, AI Research Engineers, Research Scientists, Applied Scientists, Deep Learning Researchers, NLP Researchers, Computer Vision Researchers, Reinforcement Learning Researchers, and Foundation Model Researchers. ML systems backgrounds may include: ML Systems Engineers, Machine Learning Infrastructure Engineers, AI Infrastructure Engineers, ML Platform Engineers, AI Platform Engineers, Model Serving Engineers, Model Deployment Engineers, Inference Engineers, Inference Optimization Engineers, Training Infrastructure Engineers, and Distributed Training Engineers. Performance-focused backgrounds may include: ML Performance Engineers, AI Performance Engineers, GPU Performance Engineers, Systems Performance Engineers, Model Optimization Engineers, Deep Learning Performance Engineers, HPC Engineers, High-Performance Computing Engineers, and Performance Optimization Specialists. LLM-focused backgrounds may include: LLM Engineers, Large Language Model Engineers, LLM Infrastructure Engineers, LLM Systems Engineers, LLM Inference Engineers, LLM Optimization Engineers, Generative AI Engineers, Foundation Model Engineers, AI Agent Engineers, and Language Model Researchers. Additional relevant backgrounds include: NLP Engineers, Computer Vision Engineers, Speech ML Engineers, Recommendation Engineers, Ranking Engineers, Search Engineers, Data Scientists, Quantitative Researchers, MLOps Engineers, Backend Engineers, Systems Engineers, Distributed Systems Engineers, and Scientific Computing Engineers with strong hands-on machine learning experience. Relevant Machine Learning Experience Strong candidates may have experience with: Model trainingFine-tuningSupervised learningSelf-supervised learningDeep learningTransformer architecturesLarge language modelsNeural networksModel inferenceBatch inferenceOnline inferenceModel servingDistributed inferenceDistributed trainingMulti-GPU trainingEvaluation pipelinesModel benchmarkingHyperparameter tuningData preprocessingFeature engineeringTraining-data pipelinesCheckpointingModel serializationModel conversionQuantizationDistillationPruningMemory optimizationThroughput optimizationLatency optimization Requirements Master’s degree or PhD in Computer Science, Machine Learning, Artificial Intelligence, Applied Mathematics, Statistics, Engineering, Physics, or a closely related quantitative disciplineStrong professional or research experience in machine learningStrong practical proficiency with PythonMeaningful experience with at least two modern ML frameworks, libraries, or inference toolsStrong understanding of machine learning model training, evaluation, numerical computation, or inferenceAbility to debug ML systems beyond basic high-level API usageAbility to understand tensor operations, model behavior, and implementation detailsExperience creating reproducible technical workflowsAbility to evaluate whether an implementation is objectively correctStrong technical communication and documentation skillsAbility to clearly explain implementation decisions, performance tradeoffs, and failure modes Preferred Background Experience at a major technology company, AI lab, research organization, or engineering-intensive companyExperience developing production ML systemsExperience training or serving large neural networksExperience working with large language modelsExperience with PyTorch and JAXExperience with vLLM, SGLang, llama.cpp, TensorRT-LLM, or similar inference frameworksExperience with GPU optimization or CUDAExperience with distributed training or inferenceExperience implementing ML components rather than only calling high-level APIsExperience reviewing ML code written by other engineers or researchersExperience designing ML benchmarks, evaluations, or technical assessmentsExperience profiling and optimizing production machine learning systemsSignificant open-source machine learning contributionsStrong academic ML research experiencePublished research in machine learning, AI, numerical computing, or related areas This opportunity is ideal for experienced machine learning engineers and researchers who understand models at the implementation and systems level, can debug difficult ML failures, and can determine whether a solution is correct, performant, reproducible, and professionally engineered. We are a referral partner of the client.