Job Description

We are seeking an exceptional Senior Software Engineer to build the foundational infrastructure for our next-generation AI-powered transcriptome analysis platform. This role combines cutting edge software engineering with the demands of processing petabyte-scale genomic data and orchestrating complex AI workflows. You will create the robust, scalable systems that enable our LLM and Agentic AI components to transform biological research from traditional pipelines to intelligent, autonomous discovery platforms.

Key Responsibilities

Platform Architecture & Development

Design and implement distributed systems for processing petabyte-scale genomic datasets

Build high-performance APIs supporting 10,000+ concurrent AI agent requests

Develop microservices architecture for modular AI component integration

Create real-time data streaming pipelines for continuous genomic analysis

Implement fault-tolerant systems with 99.99% uptime requirements

AI Infrastructure Engineering

Build scalable infrastructure for LLM deployment and inference optimization

Develop orchestration systems for multi-agent AI workflows

Create GPU/TPU cluster management for distributed AI processing

Implement caching strategies for billion-parameter model inference

Design model versioning and A/B testing frameworks

Data Engineering & Processing

Develop high-throughput pipelines for RNA-seq data processing

Implement efficient storage solutions for 20,000+ gene expression matrices

Create data validation and quality control frameworks

Build real-time monitoring for genomic data integrity

Design compression algorithms for efficient genomic data storage

Integration & Interoperability

Create unified APIs connecting LLMs, agents, and biological databases

Implement FHIR-compliant interfaces for clinical data integration

Build connectors for major genomic databases (GEO, TCGA, GTEx)

Develop webhook systems for laboratory instrument integration

Create SDKs for researcher and clinical user access

Required Qualifications

Technical Expertise

BS/MS in Computer Science, Software Engineering, or related field

5+ years of software engineering experience with Python as primary language

Expert-level proficiency in Python async programming and frameworks (FastAPI, asyncio)

Strong experience with distributed systems (Kubernetes, Docker, microservices)

Proven track record with high-throughput data processing systems

Deep understanding of database systems (PostgreSQL, MongoDB, Redis)

Infrastructure & DevOps

Experience with cloud platforms (AWS, GCP, or Azure) at scale

Proficiency with infrastructure as code (Terraform, Pulumi)

Strong background in CI/CD pipelines and GitOps practices

Experience with observability tools (Prometheus, Grafana, ELK stack)

Knowledge of message queuing systems (Kafka, RabbitMQ, Celery)

AI/ML Engineering

Experience deploying and scaling ML models in production

Familiarity with ML frameworks (PyTorch, TensorFlow) from an engineering perspective

Understanding of GPU programming and optimization

Experience with model serving frameworks (TorchServe, TensorFlow Serving, Ray Serve)

Preferred Qualifications

Experience with bioinformatics tools and pipelines

Knowledge of genomic data formats (FASTQ, BAM, VCF)

Familiarity with scientific computing (NumPy, SciPy, Pandas)

Understanding of HIPAA compliance and healthcare data security

Experience with real-time systems and streaming architectures

Background in building developer platforms and APIs

Contributions to open-source projects

Key Performance Metrics

Achieve <100ms API response time for 95th percentile requests

Support 1M+ daily genomic analyses with linear scaling

Maintain 99.99% platform uptime with zero data loss

Reduce infrastructure costs by 40% through optimization

Enable 5x faster genomic pipeline execution

Successfully integrate 10+ external biological databases

Integration Responsibilities

Team Collaboration

Partner with LLM Engineers to optimize model serving infrastructure

Support Agentic AI Engineers with scalable agent execution platforms

Collaborate with Bioinformaticians on pipeline optimization

Work with Security teams on HIPAA-compliant implementations

Platform Leadership

Define engineering standards and best practices

Mentor junior engineers on distributed systems design

Lead architecture reviews and technical decision-making

Drive adoption of new technologies and methodologies

Technical Stack

Core Technologies

Languages: Python (primary), Go, Rust (performance-critical components)

Frameworks: FastAPI, Celery, Ray, Dask

Databases: PostgreSQL, MongoDB, Redis, InfluxDB

Infrastructure: Kubernetes, Docker, Terraform, ArgoCD

Monitoring: Prometheus, Grafana, OpenTelemetry

ML/AI: PyTorch, Ray Serve, MLflow, Weights & Biases

Domain-Specific Tools

Genomics: Nextflow, Snakemake, CWL

Data Formats: Apache Parquet, HDF5, Zarr

Compute: SLURM, AWS Batch, Google Cloud Life Sciences

What We Offer

Build infrastructure powering the future of precision medicine

Work with cutting-edge AI and genomics technologies

Collaborate with world-class engineers and scientists

Comprehensive benefits with equity participation

$5,000 annual learning and development budget

Top-tier hardware and development environment

Flexible remote work with quarterly team offsites

The Engineering Challenge

This role offers unique engineering challenges at the intersection of:

Scale: Processing petabytes of genomic data daily

Performance: Sub-second response times for complex biological queries

Reliability: Clinical-grade system reliability

Innovation: Enabling autonomous AI agents in biological discovery

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