Machine Learning Engineers, MLOps & LLM Engineers
We are seeking experienced Machine Learning Engineers, AI Engineers, MLOps Engineers, LLM Engineers, and Applied AI Developers to evaluate and improve frontier AI coding agents through realistic technical tasks.
You will use advanced coding agents to solve and review machine learning engineering workflows involving model training, inference, deployment, MLOps, LLM applications, and production AI systems.
What You’ll Do
- Use frontier AI coding agents to complete complex machine learning engineering tasks
- Review AI-generated implementations for correctness, scalability, reliability, and performance
- Evaluate model training, inference, deployment, and production ML workflows
- Identify bugs, edge cases, architectural weaknesses, performance bottlenecks, and failure modes
- Compare outputs from multiple AI coding models
- Assess technical tradeoffs and determine which implementation is stronger
- Debug AI-generated Python, ML, data, and infrastructure code
- Apply real-world engineering judgment to production-style AI and ML scenarios
- Provide structured technical evaluations and clear written reasoning
- Test whether generated solutions actually work in realistic environments
Who Can Apply
Relevant backgrounds include:
Machine Learning Engineers, Senior Machine Learning Engineers, ML Engineers, AI Engineers, Artificial Intelligence Engineers, Applied AI Engineers, LLM Engineers, Generative AI Engineers, Deep Learning Engineers, Applied Machine Learning Engineers, and AI Software Engineers.
We also welcome:
MLOps Engineers, ML Platform Engineers, Machine Learning Infrastructure Engineers, AI Infrastructure Engineers, Model Deployment Engineers, Model Serving Engineers, ML Systems Engineers, ML Reliability Engineers, ML Production Engineers, AI Platform Engineers, and Model Operations Engineers.
LLM and GenAI backgrounds may include:
LLM Application Engineers, LLMOps Engineers, AI Agent Engineers, Agentic AI Engineers, RAG Engineers, Prompt Engineers with strong coding experience, AI Product Engineers, AI Backend Engineers, Conversational AI Engineers, NLP Engineers, and Foundation Model Engineers.
Additional relevant roles include:
Data Scientists, Applied Scientists, Research Engineers, Research Scientists, Computer Vision Engineers, NLP Engineers, Speech ML Engineers, Recommendation Engineers, Ranking Engineers, Search Engineers, Data Engineers, Backend Engineers, Software Engineers, Platform Engineers, and Distributed Systems Engineers with strong production machine learning experience.
Relevant Machine Learning Experience
Experience with one or more of the following is valuable:
- Model training and fine-tuning
- Deep learning
- Supervised and unsupervised learning
- Transformer architectures
- Large language models
- Retrieval-augmented generation
- AI agents and tool use
- Model inference and serving
- Batch and real-time prediction systems
- Feature engineering and feature stores
- Model evaluation and benchmarking
- Experiment tracking
- Hyperparameter optimization
- Data preprocessing and training pipelines
- Distributed training
- GPU-based workloads
- Model monitoring and observability
- Model versioning
- Production ML pipelines
- ML API development
- Scalability and latency optimization
- Failure analysis and debugging
- AI safety and model evaluation
AI Coding Agent Experience
Regular use of AI coding tools is strongly preferred, including:
Cursor, Claude Code, Codex, Windsurf, Gemini CLI, GitHub Copilot, Cline, Roo Code, Aider, Replit, or similar AI coding agents.
Requirements
- 2+ years of professional machine learning engineering or closely related experience
- Hands-on experience building real ML, AI, or data-driven software systems
- Experience with model training, production inference, ML infrastructure, LLM applications, or AI-powered products
- Strong Python programming skills
- Ability to understand and debug unfamiliar machine learning codebases
- Familiarity with modern AI coding agents
- Ability to evaluate AI-generated implementations and technical tradeoffs
- Strong understanding of software engineering fundamentals
- Strong debugging, analytical, and technical reasoning skills
- Clear written communication and attention to detail
Preferred Background
- Experience deploying machine learning systems to production
- Experience operating high-scale or latency-sensitive inference systems
- Experience with MLOps and ML platform infrastructure
- Experience building LLM, RAG, or AI agent applications
- Experience with distributed training or GPU workloads
- Experience reviewing code written by other ML engineers
- Experience designing ML benchmarks or evaluation frameworks
- Experience with model observability, monitoring, and production debugging
- Prior work evaluating AI-generated code or frontier coding agents
- Experience with research-to-production machine learning workflows
This opportunity is ideal for experienced ML engineers who already use AI coding agents heavily and can quickly determine whether an AI-generated machine learning solution is technically correct, production-ready, and well engineered. We are a referral partner of the client.
