Impress employers and recruiters.
Choose from hundreds of resume examples.

Target Resume Now

Tailor your resume to this Senior Software Engineer — Data Platform & Infrastructure role.

Rezi rewrites your resume against Lumos AI Research's job description. Free.

Resume score gauge reading 58 out of 100

Don't guess if your resume is good enough.

See how it scores against the Senior Software Engineer — Data Platform & Infrastructure posting at Lumos AI Research — free, in seconds.

About the Role

We're looking for a Senior Software Engineer — Data Platform & Infrastructure to build the platform that powers the creation, curation, evaluation, and delivery of high-quality data for frontier AI models. You'll work on a sophisticated platform that coordinates domain experts, AI models, automated evaluation systems, and complex data production workflows. This isn't a conventional annotation tool or CRUD application. We're building an intelligent data production system where human expertise, model-generated content, programmatic validation, and automated feedback loops work together at scale. You'll have significant ownership across the product and infrastructure stack, working directly with the founding team, researchers, and domain experts to turn complex data creation processes into intuitive, reliable, and scalable software.

Responsibilities

Design and implement core product features that enable experts to create, annotate, review, validate, and refine complex training and evaluation datasets.
Build flexible, multi-stage pipelines combining human contributions, AI-generated content, automated evaluation, and expert review.
Develop tools that enable experts to collaborate effectively with AI models, including AI-assisted annotation, content generation, feedback, and validation.
Build robust systems for dataset storage, versioning, lineage, transformations, quality control, and delivery.
Implement assignment, routing, dependencies, review queues, quality gates, and workflow automation across distributed teams of experts.
Integrate model-based judges, programmatic verifiers, consensus mechanisms, and quality metrics to ensure data reliability.
Build fast, intuitive interfaces for complex scientific and healthcare workflows, balancing flexibility with simplicity.
Develop infrastructure for model inference, structured generation, prompt execution, and automated feedback loops.
Improve performance, reliability, observability, and developer experience as data volume, workflow complexity, and expert participation grow.
Drive technical decisions from product design and system architecture through implementation, deployment, and iteration.

Requirements

5+ years of software engineering experience, or equivalent evidence of exceptional engineering ability.
Strong proficiency in TypeScript and Python, with experience building sophisticated, production-grade applications.
Experience with modern frontend frameworks such as React or Next.js, and a strong understanding of frontend architecture and application state management.
Solid backend engineering experience, including APIs, relational databases, asynchronous processing, and distributed services.
Experience designing complex data models, workflow systems, and scalable application infrastructure.
Strong understanding of PostgreSQL, queues, background jobs, caching, and cloud infrastructure.
Ability to build thoughtful user experiences for technically complex products.
Strong product intuition and the ability to translate ambiguous requirements into elegant, maintainable solutions.
Demonstrated ability to independently architect, ship, and iterate on complex systems.
Experience building data annotation, labeling, evaluation, or human-in-the-loop platforms.
Experience with AI training data pipelines, dataset curation, synthetic data generation, or model evaluation.
Familiarity with LLM APIs, structured generation, tool calling, prompt management, and AI-assisted workflows.
Experience building workflow orchestration systems, task queues, or configurable workflow engines.
Knowledge of data versioning, provenance, lineage, schema validation, and reproducibility.
Experience designing collaborative tools involving review, approvals, permissions, and complex state transitions.
Background in developer tools, internal platforms, scientific software, or other technically demanding products.
Experience working with healthcare, life sciences, or scientific datasets.
Intellectually curious, independent builders who enjoy difficult problems, move quickly, and take pride in creating exceptional software.
Engineers who can think like product designers, reason like systems engineers, and execute like founders.

Skills

TypeScript
Python
React
Next.js
Frontend architecture
Application state management
APIs
Relational databases
Asynchronous processing
Distributed services
Data modeling
Workflow systems
Scalable application infrastructure
PostgreSQL
Queues
Background jobs
Caching
Cloud infrastructure
User experience design
Product intuition
System architecture
Data annotation
Data labeling
Human-in-the-loop platforms
AI training data pipelines
Dataset curation
Synthetic data generation
Model evaluation
LLM APIs
Structured generation
Tool calling
Prompt management
AI-assisted workflows
Workflow orchestration systems
Task queues
Configurable workflow engines
Data versioning
Data provenance
Data lineage
Schema validation
Reproducibility
Collaborative tools
Review systems
Approval systems
Permissions management
State transition management
Developer tools
Internal platforms
Scientific software
Healthcare datasets
Life sciences datasets
Scientific datasets

Experience Level

Senior

Benefits

Competitive compensation
Meaningful equity

About the Company

Lumos AI Research is building the infrastructure to train and evaluate frontier AI systems in healthcare and life sciences.
As AI systems become increasingly capable, progress depends not only on better models, but on the quality of the data, environments, and feedback used to train them.
We build the infrastructure that makes this possible.
Our work spans clinical reasoning, genomics, drug discovery, and laboratory automation.
We combine deep domain expertise with software infrastructure to generate high-quality training data, evaluate model behavior, and develop reinforcement learning environments for increasingly autonomous AI systems.
We're a small, ambitious team of engineers, scientists, and researchers working to expand what AI can accomplish in the real world.