Senior Software Engineer, Data Processing

Protege

About The Position

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.

Requirements

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

Responsibilities

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
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
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
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