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About This Role
Our Mission
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Rebuild how the world works, to make institutions work better for the people they serve.
About Brain Co.
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Brain Co. builds AI\-native operating systems for large, regulated institutions. Each system is built for a specific industry, powered by agents that push real workflows forward. Underneath it all is Atlas, our proprietary platform that keeps customers in control, secure by design, and never locked into one model.
Why Now
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Brain Co. is entering its next phase of production deployments on a national scale with an elite team built from Palantir, Google, Meta, and Nvidia, and a growing footprint across government, insurance, health, and financial services.
Joining now means shaping both the company and a new category of applied AI. Every project here ships to production and is expected to create measurable customer value and impact.
You'll work alongside exceptional peers on some of the hardest problems in applied AI. It’s the kind of work you'll still be proud of in ten years from now.
About the Role:
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As our Security Engineer, Infrastructure, you'll secure the platform layer end\-to\-end including cloud infrastructure, Kubernetes, identity, networking, and the systems that AI runs on. This is a hands\-on builder role. You'll write code, ship infrastructure, and work shoulder\-to\-shoulder with our infra team as a true engineering partner, not a reviewer. It's a high\-ownership role for someone who wants to build secure\-by\-default infrastructure from first principles, and grow the function as the company scales. You'll also be thinking a step ahead about security in an AI\-native environment, where agents write code and operate inside developer and production workflows, while staying grounded in the core work of cloud, Kubernetes, identity, and networking.
What You’ll Work On:
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- Build the security foundations of our AI platform: identity, isolation, secrets management, and access control across cloud and Kubernetes environments.
- Harden infrastructure end\-to\-end from cloud networking and service meshes to CI/CD pipelines and the data and model pipelines powering Brain Co's AI capabilities work.
- Implement Zero Trust principles and machine identity across workloads; short\-lived credentials, least\-privilege access, and encrypted service communication.
- Protect customer data and the agent workloads running on our platform. Design secure execution environments and data access pathways for code and actions taken by agents on our systems.
- Build security guardrails directly into infrastructure and deployment workflows using Infrastructure\-as\-Code (Terraform) so security scales with the platform.
- Own threat modeling across infrastructure layers—identifying and remediating risks before they become incidents.
- Own security for Brain Co's forward\-deployed infrastructure — designing and hardening bespoke VPCs and customer\-environment deployments where every engagement brings its own unique attack surface.
- Partner with platform, infra, product, and ML teams to ship code and infrastructure together as a true partner so engineers move fast with secure\-by\-default systems.
You Might Be a Great Fit If You:
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- Have 5\+ years of experience in security engineering, infrastructure, or SRE with hands\-on experience building or securing production systems at scale.
- Are fluent in cloud security fundamentals (IAM, networking, KMS, secrets, isolation) across AWS, GCP, or Azure.
- Have designed and implemented secure systems end\-to\-end not just reviewed them after the fact.
- Have hands\-on experience with Kubernetes, container security, and Linux systems.
- Think in terms of threat models, trust boundaries, and failure modes and can translate that into concrete controls.
- Enjoy building paved roads and guardrails using Infrastructure\-as\-Code (Terraform preferred) so the whole team moves faster safely.
- Have experience with authentication and authorization systems, secrets management, and cryptography basics.
- Are a hands\-on builder who is energized by writing code, shipping infrastructure, and partnering directly with infra and platform engineers rather than advising from the sidelines.
- Thrive in ambiguous, high\-agency environments and want ownership of the infrastructure security function as it grows.
Bonus Points For:
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- Experience with ML systems or multi\-tenant platform isolation.
- Familiarity with service mesh and zero\-trust architectures.
- Proficiency in Go, Python, or a similar language for automation and tooling.
Why Join Us:
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- Secure an AI platform operating at the frontier; deployed in governments, hospitals, and critical industries worldwide.
- Work on infrastructure security where the attack surface is still being defined — protecting systems built to push AI capabilities forward.
- Work alongside senior engineers from Tesla, DeepMind, Databricks, and other top engineering orgs.
- Shape how infrastructure security is done from the ground up with real ownership and scope to grow.
- Ship fast, learn constantly, and see your work protect production systems used by millions.
- Earn competitive compensation and meaningful equity in a high\-growth company.
Benefits
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- Competitive salary plus equity
- Daily lunches
- Commuter benefits
- 401(k)
- Medical, Dental, and Vision
- Unlimited PTO
Role Details
About This Role
AI/ML Engineers build and deploy machine learning models in production. They work across the full ML lifecycle: data pipelines, model training, evaluation, and serving infrastructure. The role has evolved significantly over the past two years. Where ML Engineers once spent most of their time on model architecture, the job now tilts heavily toward inference optimization, cost management, and integrating LLM capabilities into existing systems. Companies want engineers who can ship production systems, and the experimenter-only role is fading fast.
Day-to-day, you're writing training pipelines, debugging data quality issues, setting up evaluation frameworks, and figuring out why your model performs differently in staging than it did on your dev set. The best ML engineers are obsessive about reproducibility and measurement. They instrument everything. They know that a model is only as good as the data feeding it and the infrastructure serving it.
Across the 4,317 AI roles we're tracking, AI/ML Engineer positions make up 70% of the market. At BRAIN, this role fits into their broader AI and engineering organization.
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
What the Work Looks Like
A typical week might include: debugging a data pipeline that's silently dropping 3% of training examples, running A/B tests on a new model version, writing documentation for a feature flag system that lets you roll back model deployments, and reviewing a junior engineer's PR for a new evaluation metric. Meetings tend to be cross-functional since ML touches product, engineering, and data teams.
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
Skills Required
Python and PyTorch dominate the requirements. Most roles expect experience with cloud platforms (AWS, GCP, or Azure) and familiarity with ML frameworks like TensorFlow or JAX. RAG (Retrieval-Augmented Generation) has become a top-3 skill requirement as companies integrate LLMs into their products. Docker and Kubernetes show up in about a third of postings, reflecting the production focus of the role.
Beyond the core stack, employers increasingly want experience with experiment tracking tools (MLflow, Weights & Biases), feature stores, and vector databases. Fine-tuning experience is valuable but less common than you'd think from reading Twitter. Most production LLM work is RAG and prompt engineering, not fine-tuning. If you have both, you're in a strong position.
Companies that are serious about AI/ML hiring tend to post specific infrastructure details in the job description: the frameworks they use, their model serving stack, their data pipeline tools. Vague postings that just say 'ML experience required' without specifics are often companies that haven't figured out what they need yet.
Compensation Benchmarks
AI/ML Engineer roles pay a median of $214,900 based on 6,420 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $194,400.
Across all AI roles, the market median is $215,000. Top-quartile compensation starts at $266,300. The 90th percentile reaches $320,790. For comparison, the highest-paying categories include AI Safety ($287,500) and Research Engineer ($272,100). By seniority level: Entry: $110,000; Mid: $194,400; Senior: $227,400; Director: $274,554; VP: $241,000.
BRAIN AI Hiring
BRAIN has 2 open AI roles right now. They're hiring across AI/ML Engineer. Positions span San Francisco, CA, US, US.
Location Context
AI roles in Austin pay a median of $214,343 across 143 tracked positions.
Career Path
Common paths into AI/ML Engineer roles include Data Scientist, Software Engineer, Research Engineer.
From here, career progression typically leads toward ML Architect, AI Engineering Manager, Principal ML Engineer.
The fastest path into ML engineering is through software engineering with a self-directed ML education. A CS degree helps, but production engineering skills matter more than academic credentials. Build something that works, deploy it, and measure it. That portfolio project is worth more than a Coursera certificate. For career growth, the fork comes around the senior level: go deep on technical complexity (staff/principal track) or move into managing ML teams.
What to Expect in Interviews
Expect system design questions around ML pipelines: how you'd build a training pipeline for a specific use case, handle data drift, or design A/B testing infrastructure for model deployments. Coding rounds typically involve Python, with emphasis on data manipulation (pandas, numpy) and algorithm implementation. Take-home assignments often ask you to build an end-to-end ML pipeline from raw data to deployed model.
When evaluating opportunities: Companies that are serious about AI/ML hiring tend to post specific infrastructure details in the job description: the frameworks they use, their model serving stack, their data pipeline tools. Vague postings that just say 'ML experience required' without specifics are often companies that haven't figured out what they need yet.
AI Hiring Overview
The AI job market has 4,317 open positions tracked in our dataset. By seniority: 138 entry-level, 2,071 mid-level, 1,655 senior, and 453 leadership roles (Director, VP, C-Level). Remote roles make up 15% of the market (635 positions). The remaining 3,657 roles require on-site or hybrid attendance.
The market median for AI roles is $215,000. Top-quartile compensation starts at $266,300. The 90th percentile reaches $320,790. Highest-paying categories: AI Safety ($287,500 median, 34 roles); Research Engineer ($272,100 median, 227 roles); AI Engineering Manager ($244,000 median, 23 roles).
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
The AI Job Market Today
The AI job market spans 4,317 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (3,004), Data Scientist (345), AI Software Engineer (309). These three account for the majority of open positions, though smaller categories often have higher per-role compensation because of specialized skill requirements.
The seniority mix tells a story about where AI teams are in their maturity. Entry-level roles (138) are outnumbered by mid-level (2,071) and senior (1,655) positions, reflecting that most companies are past the 'build a team from scratch' phase and need experienced engineers who can ship production systems. Leadership roles (Director, VP, C-Level) total 453 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 15% of all AI roles (635 positions), with 3,657 requiring on-site or hybrid attendance. The remote share has stabilized after the post-pandemic correction. Senior and specialized roles (Research Scientist, ML Architect) are more likely to be remote-eligible than entry-level positions, partly because experienced hires have more negotiating power and partly because these roles require less hands-on mentorship.
AI compensation is structured in clear tiers. The market median sits at $215,000. Top-quartile roles start at $266,300, and the 90th percentile reaches $320,790. These figures include base salary with disclosed compensation. Total compensation (including equity, bonuses, and sign-on) runs 20-40% higher at companies that offer those components.
Category matters for compensation. AI Safety roles lead at $287,500 median, while Prompt Engineer roles sit at $145,000. The spread between highest and lowest-paying categories reflects the premium on specialized technical skills versus broader analytical roles.
The most in-demand skills across all AI postings: Python (2,249 postings), Aws (1,224 postings), Azure (938 postings), Rag (915 postings), Gcp (660 postings), Pytorch (640 postings), Prompt Engineering (624 postings), Kubernetes (559 postings). Python dominates, appearing in the vast majority of role descriptions regardless of category. Cloud platform experience (AWS, GCP, Azure) is the second most common requirement. The newer entrants to the top skills list (RAG, vector databases, LLM APIs) reflect the shift from traditional ML toward generative AI applications.
Frequently Asked Questions
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