Senior ML Platform Engineer

techire ai; San Francisco (CA); On-site; USD 270,000 - 330,000; Full time

Benefits offered by this job

Stock options

Job summary

techire ai in the SF Bay Area is seeking a senior engineer to build the ML infrastructure that researchers rely on daily. You will craft scalable systems for training, evaluating and deploying advanced models, enabling secure and explainable agentic AI across industries.

You will work across distributed GPU training, data pipelines, model serving and CI/CD, collaborating with Research Scientists, Applied Scientists and ML Engineers to remove bottlenecks and accelerate innovation.

Qualifications

• Senior engineer with production ML infra experience.
• Experience building distributed ML platforms for research teams.
• Ability to scope improvements across training, evaluation and deployment.

Responsibilities

• Build infrastructure to train, evaluate and deploy ML models.
• Collaborate with Research Scientists, Applied Scientists and ML Engineers.
• Manage data pipelines, experiment tracking and model serving.
• Implement CI/CD, observability and tooling for ML workloads.

Skills

Distributed systems

ML infrastructure

Python

PyTorch

Kubernetes

Docker

AWS

Terraform

Research collaboration

Tools

MLflow

Weights&Biases

Triton

vLLM

Job description

Fortune 100 and 500 customers. Now they need the ML platform to support the next stage of research.

You'll join an AI company building secure, explainable agentic systems for industries including aerospace, manufacturing, healthcare, automotive and defence.

Their research teams are working across reasoning models, LLM post-training, reinforcement learning and multimodal 3D. Your role is to build the infrastructure that helps them train, evaluate and deploy those models efficiently.

This is broader than traditional MLOps.

You'll work across:

• Distributed GPU training, orchestration and data pipelines
• Experiment tracking, dataset versioning and evaluation
• Model serving, inference and deployment
• CI/CD, observability and internal ML tooling

The environment includes Python, PyTorch, Ray, Kubernetes, Docker, AWS and Terraform, with tools such as MLflow/W&B, Triton and vLLM.

They're looking for senior engineers who have built infrastructure for modern ML teams and are comfortable working across distributed systems, cloud infrastructure, GPU workloads and production ML.

You'll work directly with Research Scientists, Applied Scientists and ML Engineers, removing bottlenecks and helping shape the platform as the research organisation grows.

Comp. Up to $300,000 DOE + Stock

Location: SF Bay Area

If you want to build ML infrastructure that researchers genuinely depend on every day, this is worth a conversation.