Staff ML Platform Engineer

About TrueFoundry

Every production AI system, whether it's powering customer support, writing code, analyzing financial data, or diagnosing medical conditions, needs the same foundational infrastructure.A way to route between models. A way to manage tools and integrate them securely. A way to orchestrate agents and enforce governance. A unified compute layer to run it all.

That infrastructure layer is being built right now.

We are looking for a Staff ML Platform Engineer to join the team.

The Problem We're Solving

Companies are moving beyond simple chatbots to production agentic systems. These systems route between OpenAI, Anthropic, Google, and self-hosted models. They integrate dozens of tools via protocols like MCP. They orchestrate multi-agent workflows where agents coordinate with other agents.

The infrastructure to support this doesn't exist yet. You can't just duct-tape together a few API calls and call it production-ready.

You need a control plane that handles:

• Intelligent routing with observability, cost policies, and fallback logic
• Centralized tool and MCP server management with security and lifecycle controls
• Agent orchestration with governance and guardrails
• A unified compute layer to run self-hosted models, custom tools, and agents

AI Gateway is the control plane, five composable components (Prompts, LLM Gateway, MCP Gateway, Guardrails, Agent Gateway) that handle routing, orchestration, and governance.

We're Series A, backed by Intel Capital and Sequoia. Companies like CVS, Mastercard, Siemens, Paytm, Synopsys, and Zscaler run production AI workloads on our platform.

We're looking for ML 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 a platform for training and fine-tuning large-scale ML models across multi-GPU, multi-node clusters with PyTorch, Kubeflow, and other orchestration tools.
• Own the infrastructure and code that enable high-throughput, low-latency inference pipelines for state-of-the-art models.
• Build a 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.

Traits we are looking for: Ownership, ability to execute, hustle and think out of the box, data-driven decision making, be comfortable with more unknowns than knowns.

Perks of Working at TrueFoundry

• Join a fast-growing Series A, Bay Area-based startup building cutting-edge AI infrastructure.
• Comprehensive health insurance for you and your family.
• Flexible hybrid work, 3 days a week in the office (Tuesday, Wednesday and Thursday), with flexibility around the schedule.
• Lunch and snacks are on us whenever you come into the office.
• Work from another TrueFoundry office for up to 3 months each year, whether that's our London or Bay Area office, if you'd like a change of scenery.