Senior Software Engineer, Data Processing

Company Overview:

We are building Protege to solve the biggest unmet need in AI — getting access to the right training data. The process today is time intensive, incredibly expensive, and often ends in failure. The Protege platform facilitates the secure, efficient, and privacy-centric exchange of AI training data.

Solving AI’s data problem is a generational opportunity. We’re backed by world-class investors and already powering partnerships with some of the most ambitious teams in AI. The company that succeeds will be one of the largest in AI — and in tech.

We’re a lean, fast-moving, high-trust team of builders who are obsessed with velocity and impact. Our culture is built for people who thrive on ambiguity, own outcomes, and want to shape the future of data and AI.

About the Role

Protege is hiring a Senior Software Engineer to own the data processing layer at ingestion — the part of the platform that takes large-scale source data and turns it into clean, structured, enriched, validated, AI-ready datasets. This is a hands-on, backend- and data-heavy role with end-to-end ownership of the pipelines that move and process data at volume.

Protege connects organizations that hold high-value data with the AI builders who need it. The value of that exchange depends on what happens at ingestion: raw, varied, high-volume source data has to be processed reliably, securely, and at scale before it's useful to anyone.

You'll work across imaging, audio, video, and other data modalities, crossing healthcare, media, and other disparate industries and data partners. You’ll partner closely with product, Data Lab, and partner engineering teams to build robust ingestion and processing systems for structured and unstructured data at massive scale, from millions to billions of records, files, and other source objects. This role is ideal for engineers who are energized by messy data at scale, want deep ownership of critical infrastructure, and like turning ambiguity into reliable systems.

What You'll Do

Ingestion & Processing Systems

• Design, build, and operate the ingestion systems that process large volumes of multimodal data into usable, well-structured datasets

• Own the ingestion path end to end, from how data lands to how it is validated, processed, tracked, and made available downstream

• Build modality-specific processing steps for real-world source data, such as medical imaging processing, audio and video metadata extraction, quality validation, and notes processing

• Build parsers, validators, and normalization logic that can systematically handle messy, non-standard, and high-variance source formats

• Turn repeated one-off data handling work into reusable processing patterns, internal tooling, and platform capabilities

Scale, Performance & Reliability

• Build for high volume and high throughput, optimizing systems for reliability, cost, and speed

• Work across distributed and parallel compute systems to process workloads that do not fit well on a single machine

• Choose the right execution model for the workload, including batch processing, distributed execution, and modern compute patterns for unstructured data and inference-heavy processing

• Diagnose and resolve bottlenecks across ingestion and processing systems, and keep performance from degrading as volume and modality complexity grow

Data Quality, Security & Compliance

• Build validation and quality checks that catch bad, incomplete, or malformed data before it propagates downstream

• Handle sensitive and regulated data, including PHI, with the security and care the domain demands, including de-identification where required

• Track provenance, metadata, and usage constraints through the ingestion path so downstream use remains compliant and auditable

• Raise the quality bar for observability, debuggability, and operational reliability across the ingestion layer

Cross-Functional Partnership

• Partner with product and Data Lab to support new modalities, new partner requirements, and non-standard source data

• Work directly with partner engineering teams when needed to translate source-system realities into robust ingestion and processing design

• Surface recurring patterns that are worth standardizing into reusable transforms, validators, and internal tooling

• Help shape how Protege handles new data types as the platform expands into more complex data environments

What Success Looks Like

30 days: Ramp

• Get productive in the codebase and ship your first improvements to existing pipelines

• Build a working map of the ingestion and processing stack, the major data flows, and how we handle each modality

• Meet the engineering, product, and Data Lab teams to understand how the function operates across the company

60 days: Take Ownership

• Own a processing pipeline or modality end to end, from ingestion through delivery of AI-ready output

• Develop depth in how we handle one or two data types at scale

• Start raising the bar on data quality, observability, and processing best practices

90 days: Operate Independently

• Own a significant part of the ingestion and processing layer and lead design on new modalities or scaling challenges

• Ship reliably with minimal hand-holding, and help unblock others working in the data layer

• Identify at least one leverage opportunity — a reusable transform, tool, or architectural improvement — worth investing in, and drive it

What You Bring

Must Haves

• 5+ years building and operating production backend or data systems, with real experience in data processing at scale

• Hands-on experience designing and running large-scale data pipelines

• Strong programming skills in Python

• Experience with distributed data processing

• Strong proficiency with AWS

• Comfort with messy, varied, high-volume data and high ambiguity, with a knack for finding patterns in complex environments

• Attention to detail without losing speed, and a bias to action

• Excited to work on a product built around moving and processing large volumes of data

• Curious, tenacious, and proactive

Nice to Haves

• Experience processing one or more specific modalities at scale: medical imaging (e.g., DICOM), text, audio or video

• Background working with sensitive or regulated data environments (HIPAA, healthcare compliance, PHI handling)

• Experience with streaming systems or workflow orchestration (e.g., Airflow, Dagster)

• Experience with GCP and Azure

• Prior startup experience as a founding or early engineer

• Familiarity with ML, NLP, or LLM-based systems, including embeddings and fine-tuning