Platform ML Engineer — Build Production AI for Institutions

BrainCo; San Francisco (CA); On-site; USD 180,000 - 240,000; Full time

Job summary

Brain Co. in San Francisco is seeking a Machine Learning Engineer, Platform to build core ML capabilities used across products.

You will improve document extraction, blueprint foundation models, and an end-to-end improvement loop in production. You will collaborate with teams across the company, push frontier models, and ensure production readiness, performance, and reliability in institutional deployments.

Qualifications

• You understand how machine learning actually works — not just the tooling, but the philosophy underneath: what a loss function really optimizes, how generalization breaks under distribution shift, why evaluation is where systems quietly go wrong.

Responsibilities

• Turn pod needs into platform capabilities — find the general capability inside one team's urgent request, without over-abstracting before the pattern is proven.
• Own capabilities end-to-end — there is no handoff; whoever builds the capability owns its behavior in production, across every deployment that uses it.
• Work at the research frontier with production stakes, turning LLMs, RL fine-tuning, and agentic systems into capabilities that dozens of institutional workflows depend on at once.
• Serve customers on both sides of the wall — project pods as true customers, and when needed, the domain experts whose decisions your capabilities ultimately power.
• Engineer for production reality — navigating accuracy, latency, cost, and reliability across environments far messier than any benchmark.
• Raise the bar across the company — the platform is how learnings travel: what one pod discovers, you turn into something every pod inherits.

Skills

ML fundamentals

LLMs

Agent systems

Prompting

Job description

Brain Co. in San Francisco is seeking a Machine Learning Engineer, Platform to build core ML capabilities used across products.

You will improve document extraction, blueprint foundation models, and an end-to-end improvement loop in production. You will collaborate with teams across the company, push frontier models, and ensure production readiness, performance, and reliability in institutional deployments.