Member of Technical Staff – Data Platform & Annotation Tools

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 Data Platform Engineer, you’ll design the systems and tools that transform raw data into the lifeblood of our models—clean, richly labeled, and continuously refreshing datasets. Your work will span scalable ingestion pipelines, active-learning loops, human-and-AI annotation workflows, and quality-control analytics. The platform you build will power every stage of the model lifecycle—from supervised fine-tuning to retrieval-augmented generation and reinforcement learning.

This is a good role for you if you:

• Have hands-on experience building data or annotation platforms that support large-scale ML workloads
• Are fluent in Python, SQL, and modern data stacks (Spark/Flink, DuckDB/Polars, Arrow, Kafka/Airflow/Flyte)
• Understand how class balance, bias, leakage, and adversarial filtering impact ML data quality and model performance
• Have managed human-in-the-loop labeling operations—including vendor selection, rubric design, and LLM-assisted automation
• Care deeply about reproducibility and observability—tracking everything from dataset hashes to annotation agreement scores and drift detection
• Communicate clearly with both research scientists and non-technical stakeholders
  
  

Responsibilities include:

• Ingest and transform large multimodal corpora (text, code, audio, vision) using scalable ETL, normalization, and deduplication pipelines
• Build annotation tools—web UIs, task queues, consensus engines, and review dashboards—to enable fast and accurate labeling by both crowd vendors and internal experts
• Design active-learning and RLHF data loops that surface high-value samples for human review, integrate synthetic LLM feedback, and support continuous iteration
• Version, audit, and govern datasets with lineage tracking, privacy controls, and automated quality metrics (toxicity, PII, brand consistency)
• Collaborate with training, inference, and safety teams to define data specs, evaluate dataset health, and unlock new model capabilities
• Contribute upstream to open-source data and annotation tools (e.g., Flyte, Airbyte, Label Studio) and share best practices with the community

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.