ROLE OVERVIEW
You will be responsible for building, maintaining and productionizing the backend infrastructure of VAMS ATLASâ„¢.
The platform must support:
* Deterministic scientific computation
* Reproducible pipeline execution
* Immutable/versioned reference artifacts
* PostgreSQL-backed registries
* Asynchronous computational workloads
* API services
* Audit trails and provenance
* Evidence and entitlement gating
* Report generation contracts
* Cloud object storage
* Production observability
* Secure integration with frontend applications
CORE RESPONSIBILITIES
1. Backend API Development
Develop and maintain backend services and APIs supporting the VAMS BIOME ecosystem.
2. PostgreSQL & Data Architecture
Own and extend the ATLAS PostgreSQL data layer. The engineer will work with registries covering areas such as:
* samples
* sequencing runs
* analysis runs
* score definitions
* panels
* conditions
* reference cohorts
* reference artifact sets
* pipeline/version metadata
* QC flags
* confidence penalties
* report outputs
* audit records
Scientific outputs must remain traceable to the exact configuration and artifacts from which they were generated.
3. Reference Artifact Registry
Implement and maintain the VAMS ATLAS Reference Artifact Registry.
4. Deterministic Reference Selection
Implement deterministic selection of reference cohorts/artifacts based on analysis context.
5. Analysis Run Orchestration
Build and maintain the execution layer connecting backend services with computational/bioinformatics pipelines.
The engineer is not expected to invent microbiome algorithms.
Bioinformatics and scientific teams define biological processing and interpretation logic.
The backend engineer makes these pipelines reliable, deterministic, scalable and auditable.
6. Two-Mode Execution Architecture
The platform maintains strict separation on Cohort Build Mode
Used offline to construct and register approved reference artifacts.
7. Auditability & Provenance
Every ATLAS run must generate sufficient provenance to reconstruct the analysis.
Where appropriate, computational outputs should have integrity hashes.
A historical report should be traceable back to its computational inputs and configuration.
8. Scientific Governance Enforcement
The backend is responsible for enforcing scientific governance supplied by the scientific/Founder’s Office configuration.
9. Reporting Backend
Develop structured report objects by:
* VAMS ATLAS web applications
* downloadable reports
* clinician-facing interfaces
* future mobile applications
* analytics and internal systems
10. Cloud & Infrastructure
Help operate and evolve the cloud execution architecture.
Expected areas include:
* AWS
* S3/object storage
* containerized workloads
* Docker
* Asynchronous jobs/queues
* Worker infrastructure
* CI/CD
* Environment management
* Secrets management
* Log/ Monitor
* Health checks
Experience with one or more of the following is valuable:
* AWS Batch
* ECS/Fargate
* Step Functions
* Lambda
* SQS
* EventBridge
* CloudWatch
Equivalent production cloud experience is acceptable.
11. Security & Production Engineering
Implement secure engineering practices across the backend.
Experience working with health, genomic or other sensitive datasets is advantageous.
REQUIRED TECHNICAL SKILLS
Candidates should have strong practical experience with most of the following:
Backend
* Python
* FastAPI or comparable modern Python API framework
* Pydantic / schema validation
* REST API design
* asynchronous/background processing
Database
* PostgreSQL
* SQL
* relational schema design
* migrations
* constraints and indexes
* transactional systems
* JSON/JSONB
Infrastructure
* Docker
* Linux
* Git / GitHub
* CI/CD
* AWS or equivalent cloud infrastructure
* object storage such as S3
* environment and secrets management
Engineering
* Automated testing
* API testing
* Integration testing
* Structured logging
* Exception handling
* Idempotent operations
* Versioning
* Reproducible execution
STRONGLY PREFERRED EXPERIENCE
Candidates with experience in any of the following will be particularly valuable:
* Bioinformatics
* Genomics
* Microbiome data
* Scientific computing
* Health-tech
* Clinical software
* Laboratory data systems
* Workflow orchestration
* Data engineering
* ML/data pipelines
* Reproducible computational pipelines
Familiarity with concepts such as FASTQ, sequencing pipelines, reference databases or computational provenance is advantageous but not mandatory.
Important Role Boundary
This position owns engineering implementation, not independent scientific decision-making.
The Backend / Platform Engineer should not independently:
* Invent microbiome scoring formulas
* Modify biological thresholds
* Change evidence tiers
* Determine whether datasets are scientifically valid
* Redefine condition mappings
* Reinterpret microbiome biology
* Modify scientific recommendations
Those decisions are supplied through governed scientific specifications.
The engineer’s responsibility is to ensure those specifications are implemented correctly, deterministically, securely and audibly.
What Success Looks Like
Within the first few months, the successful engineer should be able to demonstrate that:
1. ATLAS backend services can reliably execute production analysis workflows.
2. Every analysis run is versioned and auditable.
3. Historical analyses can identify the exact reference artifacts used.
4. Reference artifacts cannot be accidentally modified by production user workflows.
5. Scientific gating is enforced server-side.
6. Backend APIs expose stable contracts to frontend applications.
7. Failed computational jobs can be diagnosed and recovered safely.
8. Automated tests cover critical backend and governance rules.
9. Production services have appropriate logging, monitoring and health checks.
10. The platform can scale from Phase-1 operations toward substantially larger sample volumes without architectural replacement.
Candidate Profile
We are particularly interested in engineers who enjoy building systems where correctness matters more than simply shipping endpoints.
You may be a good fit if you:
* Think carefully about system boundaries
* Understand database integrity
* Prefer deterministic systems over hidden behavior
* Write testable code
* Document architectural decisions
* Are comfortable reading technical/scientific specifications
* Can work independently
* Can challenge ambiguous requirements constructively
* Understand that scientific software requires stronger provenance than ordinary consumer applications
Minimum Qualifications
* Bachelor’s degree in Computer Science, Software Engineering, Information Technology or related discipline, or equivalent demonstrated engineering experience
* Approximately 3+ years of professional backend/software engineering experience
* Strong Python skills
* Strong PostgreSQL/SQL skills
* Experience building production APIs
* Experience with Docker and Git
* Experience deploying or operating cloud-based systems
* Good understanding of software testing and production debugging
Preferred Qualifications
* 4-6+ years backend/platform experience
* FastAPI production experience
* AWS architecture experience
* Workflow/orchestration experience
* Bioinformatics or computational biology exposure
* Experience handling sensitive health/scientific data
* Experience building reproducible data-processing systems
* Infrastructure-as-code experience
* Strong automated testing practices
* Experience with observability and production incident debugging
WHAT TO SUBMIT
Interested candidates should submit the ff:
* Resume/CV
* GitHub profile or examples of previous technical work where available
* Brief description of a backend/platform system they personally designed or substantially implemented
* Technologies used
* Their specific contribution to the architecture
Shortlisted candidates will complete a technical discussion and practical architecture/coding assessment focused on backend engineering, PostgreSQL, APIs, deterministic processing and production system design.
3 - 7 Years
1 Openings
Not Disclosed
Face-to-Face interview
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