Applied Machine Learning Engineer
Inference.net provides fully managed AI infrastructure for deploying, observing, tracing, training, and evaluating open-source, custom, and fine-tuned AI models at scale. It serves AI-native product teams looking to switch from providers like OpenAI, Anthropic, and Gemini to optimized infrastructure for lower cost and faster performance.
Funding
Projects
About Inference.net
Inference.net offers a suite of infrastructure products for AI-native teams, including Deploy (turn-key, globally distributed model hosting with high uptime), Observe (LLM observability with monitoring, tracing, and debugging tools), Trace (capturing agent LLM calls, tool calls, and framework steps), Train (custom model fine-tuning in days), Evaluate (rigorous benchmarking before production deployment), and HALO (open-source agent optimization). The platform hosts models such as GLM-5.2, Kimi K2.6, MiniMax-M2.5, and GPT-OSS 120B, and offers proprietary open-source models like ClipTagger and Schematron. Clients include engineering teams such as Cal AI and Gravity Ads, who use the platform to cut latency and costs while maintaining frontier-level model quality. The company is SOC 2 Type II compliant, indicating a focus on production-grade, enterprise-ready AI infrastructure.
Skills
About the Role
You will be responsible for building and improving the core ML systems that power our custom model training platform, while also applying these systems directly for customers. Your role sits at the intersection of applied research and production engineering. You'll lead projects from data intake to trained model, building the infrastructure and tooling along the way. Your north star is model quality at scale, measured by how well our custom models match frontier performance, how efficiently we can train and serve them, and how smoothly we can deliver results to our customers. You'll own the full training lifecycle: processing data, creating dashboards for visibility, training models using our frameworks, running evaluations, and shipping results. This role reports directly to the founding team. You'll have the autonomy, a large compute budget / GPU reservation, and technical support to push the boundaries of what's possible in custom model training.
Requirements
- 2+ years of experience training AI models using PyTorch
- Hands-on experience with post-training LLMs using SFT or RL
- Strong understanding of transformer architectures and how they're trained
- Experience with LLM-specific training frameworks (e.g., Hugging Face Transformers, DeepSpeed, Axolotl, or similar)
- Experience training on NVIDIA GPUs
- Strong data processing skills and comfortable building ETL pipelines and working with large datasets
- Track record of creating benchmarks and evaluations
- Ability to take research techniques and apply them to production systems
Responsibilities
- Lead projects from data intake through the full training pipeline, including processing, cleaning, and preparing datasets for model training
- Build and maintain data processing pipelines for aggregating, transforming, and validating training data
- Create dashboards and visualization tools to display training metrics, data quality, and model performance
- Train models using internal frameworks and iterate based on evaluation results
- Develop robust benchmarks and evaluation frameworks that ensure custom models match or exceed frontier performance
- Build systems to automate portions of the training workflow, reducing manual intervention and improving consistency
- Take research features and ship them into production settings
- Apply the latest techniques in SFT, RL, and model optimization to improve training quality and efficiency
- Collaborate with infrastructure engineers to scale training across the GPU fleet
- Deeply understand customer use cases to inform training strategies and surface edge cases
Benefits
- Equity
- Comprehensive benefits