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.