ML Engineer | 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 components
• Build reproducible training and inference workflows
• Implement model architectures, tensor operations, data pipelines, tokenization, batching, and generation systems
• Review and debug AI-generated machine learning code
• Diagnose numerical instability, incorrect tensor behavior, memory issues, and performance regressions
• Optimize inference and training for latency, throughput, memory consumption, and hardware utilization
• Compare multiple model implementations for correctness, efficiency, and reproducibility
• Build benchmarks and objective evaluation criteria
• Develop tests that verify model behavior and performance
• Debug distributed and system-level ML failures
• Document technical decisions, engineering tradeoffs, assumptions, and limitations
• Create 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 training
• Fine-tuning
• Supervised learning
• Self-supervised learning
• Deep learning
• Transformer architectures
• Large language models
• Neural networks
• Model inference
• Batch inference
• Online inference
• Model serving
• Distributed inference
• Distributed training
• Multi-GPU training
• Evaluation pipelines
• Model benchmarking
• Hyperparameter tuning
• Data preprocessing
• Feature engineering
• Training-data pipelines
• Checkpointing
• Model serialization
• Model conversion
• Quantization
• Distillation
• Pruning
• Memory optimization
• Throughput optimization
• Latency optimization

Requirements

• Master’s degree or PhD in Computer Science, Machine Learning, Artificial Intelligence, Applied Mathematics, Statistics, Engineering, Physics, or a closely related quantitative discipline
• Strong professional or research experience in machine learning
• Strong practical proficiency with Python
• Meaningful experience with at least two modern ML frameworks, libraries, or inference tools
• Strong understanding of machine learning model training, evaluation, numerical computation, or inference
• Ability to debug ML systems beyond basic high-level API usage
• Ability to understand tensor operations, model behavior, and implementation details
• Experience creating reproducible technical workflows
• Ability to evaluate whether an implementation is objectively correct
• Strong technical communication and documentation skills
• Ability 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 company
• Experience developing production ML systems
• Experience training or serving large neural networks
• Experience working with large language models
• Experience with PyTorch and JAX
• Experience with vLLM, SGLang, llama.cpp, TensorRT-LLM, or similar inference frameworks
• Experience with GPU optimization or CUDA
• Experience with distributed training or inference
• Experience implementing ML components rather than only calling high-level APIs
• Experience reviewing ML code written by other engineers or researchers
• Experience designing ML benchmarks, evaluations, or technical assessments
• Experience profiling and optimizing production machine learning systems
• Significant open-source machine learning contributions
• Strong academic ML research experience
• Published 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.