OpenAI ML Engineering Jobs
Open ml engineering roles at OpenAI, pulled live from their hiring system.
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ML Engineering roles at OpenAI roles cluster around $266k, with most listings between $230k and $295k. Most common stacks in the current ml engineering listings: Machine Learning, PyTorch, Python. Most of these ml engineering roles are on-site or hybrid; 3% are fully remote.
Researcher: Agent Post-Training, API & Power-Users
Improve agentic model capabilities for API and power users by designing experiments, building evals from real workflows, and driving post-training interventions from discovery through launch.
Software Engineer, Monetization ML Infrastructure
As a Software Engineer, you will build and design the machine learning infrastructure for OpenAI's monetization and ads systems. This involves developing large-scale data pipelines, model training platforms, real-time inference systems, and experimentation frameworks to support high-throughput, low-latency advertising workloads.
Researcher, Connectors - Agent Post-Training
Train frontier agents to use code, APIs, and enterprise tools (Slack, GitHub, Salesforce, etc.) by designing RL experiments, building evals, and owning the post-training stack that ships into Codex and ChatGPT.
Researcher, Computer Use - Agent Post-Training
Train frontier models to operate computers, navigate browsers/desktops, and complete complex workflows. Own post-training experiments, evals, RL pipelines, and ship improvements into OpenAI's agent products.
Researcher, Artifacts - Agent Post-Training
Train frontier models at OpenAI to create polished, useful artifacts like documents, spreadsheets, and dashboards. Own post-training improvements across RL, data pipelines, evals, and graders to ship production agent capabilities.
AI Systems Engineer, Codex Agents
Builds core agent harness for Codex AI agents, enabling safe tool use, code execution, and long-horizon tasks in production. Designs systems for sandboxing, evaluation, observability, and performance optimization across ML workflows and infrastructure.
Researcher, Alignment Oversight
Designs and runs experiments to improve oversight of increasingly capable AI models, including model training, evaluation, and deployment of practical systems. Analyzes failures and develops techniques to train more aligned models using oversight signals.
Research Infrastructure Engineer, Training Systems
Builds and maintains infrastructure for large-scale ML model training and experimentation. Designs APIs, improves reliability and performance of training pipelines, and debugs issues across Python, PyTorch, distributed systems, GPUs, and networking.
Software Engineer, Inference - Performance Optimization
Models inference performance across application, model, and fleet layers using microbenchmarks to build cost-to-serve estimates. Analyzes workloads end-to-end, enhances bottleneck detection tools, and collaborates on optimizations for latency, throughput, and cost.
Software Engineer, Workload Enablement
Software Engineer enabling production AI workloads on new hardware platforms through porting, benchmarking, stress testing, and performance optimization. Requires 5+ years in ML systems, distributed training, PyTorch, and RDMA/NCCL expertise.
Applied AI Engineer, Codex Core Agent
Develops and improves Codex AI agents for real-world software engineering tasks, focusing on performance, reliability, and integration with research and product teams. Requires strong Python, ML/LLM experience, and skills in evaluation, prompting, and debugging production failures.
TL, Research Inference
Leads development of high-performance inference systems for large-scale AI models, optimizing execution paths, distributed GPU inference, and operators. Partners with research teams to enable efficient exploration of new architectures grounded in real scalability constraints.
Machine Learning Engineer, Integrity
Deploys state-of-the-art ML models and fine-tunes LLMs in production to enhance platform integrity and safety against adversarial threats. Requires Master's/PhD, deep learning expertise, PyTorch/TensorFlow proficiency.
Inference Technical Lead, On-Device Transformers
Technical lead evaluating hardware platforms and co-designing transformer models for on-device deployment. Leads team building low-level inference stack, optimizing for latency, memory, and power constraints. Requires deep experience with accelerators, transformers, and performance-critical ML software.
Technical Deployment Lead, Forward Deployed Engineering (FDE)
Leads end-to-end technical delivery of complex AI systems to customers, owning planning, execution, and adoption. Requires 7+ years customer-facing technical leadership, AI shipping experience, and strong project management in high-stakes environments.
Software Engineer, Marketing Innovation
Build and own autonomous agentic systems for customer-facing revenue and marketing workflows, partnering with sales and marketing teams. Requires 4+ years experience in software/ML engineering, full-stack skills in Python/JavaScript, and production systems expertise.
Research Engineer / Machine Learning Engineer - B2B Applications
Designs and builds advanced speech models (speech-to-speech, TTS, transcription) for B2B applications at OpenAI. Requires Master's/PhD, 2+ years experience, deep learning expertise, and PyTorch/TensorFlow proficiency.
Machine Learning Engineer, Distributed Data Systems
Designs and scales distributed data infrastructure for large-scale multimodal AI training and evaluation. Collaborates with researchers to build reliable, high-performance systems in a fast-paced environment.
AI Deployment Manager - NYC
Leads technical enablement and adoption of OpenAI products like ChatGPT Enterprise, API, and Agents for enterprise customers through workshops and trainings. Requires 4+ years customer-facing experience, strong AI technical knowledge including RAG and fine-tuning, and exceptional communication skills.
AI Deployment Engineer- Codex
Partners with customers to design and scale AI-enhanced coding workflows using OpenAI Codex, builds demos and automations, leads workshops, and provides technical expertise. Requires 5+ years in technical consulting or solutions engineering and power user experience with AI coding tools.
ML Research Engineer - Hardware Codesign
Research-Hardware Codesign Engineer bridges ML research and silicon architecture, debugging performance gaps, writing quantization kernels, prototyping numerics in RTL, and analyzing system tradeoffs for AI-optimized hardware.
Training: ML Framework Engineer
Develops and optimizes internal distributed ML training framework to boost hardware efficiency and enable researchers to experiment with new AI models. Requires strong Python skills, systems understanding, and passion for performance tuning.