Staff ML Platform Engineer – Large Scale Training (LLMOps/MLOps)

Build the Future of Scalable AI at TrueFoundry

At TrueFoundry , we’re redefining how ML teams train, deploy, and scale their models. Our LLMOps and MLOps platform empowers organizations to experiment faster, train large-scale models reliably, and deploy them seamlessly on Kubernetes—with the same muscle as Big Tech.

We're looking for ML Systems Engineers who are passionate about scaling deep learning workloads, optimizing multi-GPU training, and shipping production-grade solutions. If you live and breathe PyTorch, multi-node training, and love solving gnarly infra challenges—this is your place.

What You’ll Work On

• Write clean, modular, and scalable Python code , with a strong emphasis on reliability and performance.
• Build platform for training and finetuning large-scale ML models across multi-GPU, multi-node clusters with PyTorch, Kubeflow, and other orchestration tools.
• Own the infrastructure and code that enables high-throughput, low-latency inference pipelines for state-of-the-art models.
• Build platform for developing, deploying and evaluating agentic applications for our end customers.
• Help shape internal standards and best practices across the engineering team for high-scale ML workloads.

What We’re Looking For

• 5+ years of hands-on experience building and deploying ML systems at scale.
• 5+ years of writing production quality high performance code.
• Deep experience with multi-GPU/multi-node training , ideally with PyTorch as your primary framework.
• Experience working with torch, high-level ML frameworks, and inference engines (vLLM or TensorRT).
• Experience with Kubernetes is highly preferred; exposure to Kubernetes-native tools is a huge plus.
• A pragmatic mindset—you know when to optimize and when to ship.
• Bonus: Familiarity with open-source LLM training/fine-tuning.

Why Join TrueFoundry?

• Work directly with ex-Facebook engineers and founders from IIT Kharagpur, UC Berkeley, and Y Combinator alumni .
• First-hand exposure to building and scaling a deep-tech startup —insights you’ll carry if you want to start your own one day.
• Be part of a fearlessly experimental culture focused on customer success and long-term impact.

Flexible hours, learning credits, and the opportunity to work shoulder-to-shoulder with the co-founders (Abhishek & Nikunj).