At Inflection AI, our public benefit mission is to harness the power of AI to improve human well-being and productivity.

The next era of AI will be defined by agents we trust to act on our behalf. 

We’re pioneering this future with human-centered AI models that unite emotional intelligence (EQ) and raw intelligence (IQ)—transforming interactions from transactional to relational, to create enduring value for individuals and enterprises alike.

Our work comes to life in two ways today:

Pi, your personal AI, designed to be a kind and supportive companion that elevates everyday life with practical assistance and perspectives.

Platform — large-language models (LLMs) and APIs that enable builders, agents, and enterprises to bring Pi-class emotional intelligence into experiences where empathy and human understanding matter most.

We are building toward a future of AI agents that earn trust, deepen understanding, and create aligned, long-term value for all.

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.