Inflection AI is a public benefit corporation leveraging our world class large language model to build the first AI platform focused on the needs of the enterprise. 

Who we are:

Inflection AI was re-founded in March of 2024 and our leadership team has assembled a team of kind, innovative, and collaborative individuals focused on building enterprise AI solutions. We are an organization passionate about what we are building, enjoy working together and strive to hire people with diverse backgrounds and experience. 

Our first product, Pi, provides an empathetic and conversational chatbot. Pi is a public instance of building from our 350B+ frontier model with our sophisticated fine-tuning (10M+ examples), inference, and orchestration platform. We are now focusing on building new systems that directly support the needs of enterprise customers using this same approach.

Want to work with us? Have questions? Learn more below.

About the Role

As a Platform Engineer, you’ll be part of the small team designing and building the core systems behind a brand-new enterprise ML product. You’ll work at the intersection of engineering and ML—building the services and APIs that connect our large language models with real-world workflows and user experiences. The stack is primarily Python and PyTorch, and your work will involve everything from model integration to inference orchestration and data flow.

This isn’t a deep research role, but it’s ideal for bacend engineers who are excited to work closely with ML models and want to be part of building something new, fast, and production-ready.

This is a good role for you if you:

• Have strong experience building backend systems and APIs, ideally using Python and/or TypeScript/Node.
• Familiarity with FastAPI, Java, React, Node.js, Kubernetes, Redis, and Postgres.
• Are comfortable working with PyTorch models—loading checkpoints, managing inference pipelines, and integrating outputs into production features.
• Have worked on systems that need to be fast, stable, and scalable in real-world use.
• Enjoy collaborating across disciplines—partnering with ML researchers, infra teams, and product engineers to get things shipped.
• Are excited by the opportunity to build and launch ML-powered products.

Responsibilities include:

• Building backend services that connect our ML models to user-facing features and enterprise workflows.
• Writing clean, reliable Python code to support model inference, evaluation, and iteration at scale.
• Collaborating with ML researchers on model integration and tooling to accelerate experimentation.
• Designing the glue between data, orchestration layers, and model logic for real-time and batch applications.
• Owning critical paths of the system—from deployment to observability—and helping make them robust and scalable from day one.

Employee Pay Disclosures

At Inflection AI, we aim to attract and retain the best employees and compensate them in a way that appropriately and fairly values their individual contributions to the company. For this role, Inflection AI estimates a starting annual base salary will fall in the range of approximately $175,000 - $350,000 depending on experience. This estimate can vary based on the factors described above, so the actual starting annual base salary may be above or below this range.

Interview Process

Apply: Please apply on Linkedin or our website for a specific role.

After speaking with one of our recruiters, you’ll enter our structured interview process, which includes the following stages:

1. Hiring Manager Conversation – An initial discussion with the hiring manager to assess fit and alignment.
2. Technical Interview – A deep dive with an Inflection Engineer to evaluate your technical expertise.
3. Onsite Interview – A comprehensive assessment, including:
• A domain-specific interview
• A system design interview
• A final conversation with the hiring manager

Depending on the role, we may also ask you to complete a take-home exercise or deliver a presentation.

For non-technical roles, be prepared for a role-specific interview, such as a portfolio review.

Decision Timeline
We aim to provide feedback within one week of your final interview.