> Markdown version of [/jobs/ext/2404892-machine-learning-engineer](https://www.wearedevelopers.com/jobs/ext/2404892-machine-learning-engineer). Every page supports `.md` or `Accept: text/markdown`. Links point to the HTML versions so they work for humans too. Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md). --- # Machine Learning Engineer - **Company:** BESPOKE LAB INCORPORATED - **Location:** McKinney, TX, United States - **Experience:** Experienced - **Contract:** Permanent contract - **Skills:** Training Data, Amazon Web Services, Python (Programming Language), Machine Learning, Google Cloud, Pytorch, Large Language Models, Deep Learning, Slurm - **Published:** August 1, 2026 - **Apply:** https://www.adzuna.com/details/5766191717 ## About the Role 3+ years of ML engineering experience - model training, fine-tuning, or post-training pipelines in research or production Strong Python and deep learning proficiency (PyTorch preferred; familiar with training loops, optimizers, mixed precision) Hands-on experience with LLM post-training - SFT, RLHF, PPO, DPO, or reward model training - and understanding of how training data quality affects model behavior Familiarity with RL frameworks (Gymnasium, dm_env) and the ability to design or modify reward functions for agent training objectives Experience running experiments at scale on cloud or HPC (AWS, GCP, SLURM, or Ray) Solid understanding of evaluation methodology - held-out sets, benchmark design, avoiding train/eval contamination ## Description You will work directly with our research team on RL environment and task creation for agent training. This means designing observation spaces, action spaces, reward signals, and success criteria for new environments - and building the infrastructure that makes world-scale RL training possible. This is a high-ownership role; you will be building novel systems, not maintaining legacy ones. ## Related Videos - [Adding knowledge to open-source LLMs](https://www.wearedevelopers.com/videos/1522-adding-knowledge-to-open-source-llms) - [The Cloud is Calling: Answer with In-Demand Skills](https://www.wearedevelopers.com/videos/945-the-cloud-is-calling-answer-with-in-demand-skills) - [Running Secure Life Science Research at Scale using Hybrid GPU HPC and Kubernetes 🧬](https://www.wearedevelopers.com/videos/100355-running-secure-life-science-research-at-scale-using-hybrid-gpu-hpc-and-kubernetes) - [Photonic Computing: Programming a New Class of AI Accelerators (incl. Live Coding)](https://www.wearedevelopers.com/videos/100196-photonic-computing-programming-a-new-class-of-ai-accelerators-incl-live-coding) - [Serverless deployment of (large) NLP models ](https://www.wearedevelopers.com/videos/158-serverless-deployment-of-large-nlp-models) - [Geometric deep learning for drug discovery](https://www.wearedevelopers.com/videos/264-geometric-deep-learning-for-drug-discovery) ## Related Articles - [MLops – Deploying, Maintaining And Evolving Machine Learning Models in Production](https://www.wearedevelopers.com/magazine/115-mlops-deploying-maintaining-and-evolving-machine-learning-models-in-production) - [What Are Large Language Models?](https://www.wearedevelopers.com/magazine/304-what-are-large-language-models) - [MLOps – What’s the deal behind it?](https://www.wearedevelopers.com/magazine/125-mlops-what-s-the-deal-behind-it) - [The Best Large Language Models on The Market](https://www.wearedevelopers.com/magazine/319-the-best-large-language-models-on-the-market) - [From Prototype to Production: Build AI Agents with This Free 4-Course Learning Path](https://www.wearedevelopers.com/magazine/655-from-prototype-to-production-build-ai-agents-with-this-free-4-course-learning-path) - [Got AI ideas but no money? Here are 10 free ways to level up your AI skills with Google Cloud](https://www.wearedevelopers.com/magazine/600-got-ai-ideas-but-no-money-here-are-10-free-ways-to-level-up-your-ai-skills-with-google-cloud)