Member of Technical Staff Model Training

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 Model Training engineer, you will design, build, and scale the post-training pipelines that turn a general LLM into a brand-fluent, production-ready assistant. Your innovations in fine-tuning and preference optimization (RLHF, DPO, GRPO, RLAIF) will directly improve reliability, alignment, and cost.

This is a good role for you if you:

• Have hands-on experience training and fine-tuning large transformer models on multi-GPU / multi-node clusters.


• Are fluent in PyTorch and its ecosystem tools (Torchtune, FSDP, DeepSpeed) and enjoy digging into distributed-training internals, mixed precision, and memory-efficiency tricks.


• Have shipped or published work in RLHF, DPO, GRPO, or RLAIF and understand their practical trade-offs.


• Care deeply about training tools, pipelines, and reproducibility—you automate the boring parts so you can iterate on the fun parts.


• Balance research curiosity with product pragmatism—you know when to run an ablation and when to ship.


• Communicate crisply with both technical and non-technical teammates.
  
  

Responsibilities include:

• Contribute to end-to-end post-training workflows—dataset curation, hyper-parameter search, evaluation, and rollout—using PyTorch, Torchtune, FSDP/DeepSpeed, and our internal orchestration stack.


• Prototype and compare alignment techniques (e.g., curriculum RL, multi-objective reward modeling, tool-use fine-tuning) and push the best ideas into production.


• Automate training at scale: build robust pipeline components, tools, scripts, and dashboards so experiments are reproducible and easy to trace.


• Define the metrics that matter; run A/B tests and iterate quickly to meet aggressive quality targets.


• Collaborate with inference, safety, and product teams to land improvements in customer-facing systems.

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.