Senior Backend Engineer (Agentic Data Platform)

Location: United States , United States, Latin America (LATAM), South America, Europe, Eastern Europe, Middle East

Department: Engineering

Location Type: REMOTE

Employment Type: FULL_TIME

The opportunity

What you'll own

• Develop and maintain high-performance and fault-tolerant distributed backend services.
• Build and optimize data processing pipelines over large genomic and health datasets using Apache Spark and DuckDB.
• Design data structures, storage and indexing strategies across PostgreSQL, Qdrant, and Redis for performance at scale.
• Own async event processing and workflow orchestration using AWS SQS, RabbitMQ, and BullMQ.
• Drive latency, throughput, and reliability through parallel execution, caching, and efficient data access.
• Build guardrail and quality assurance layers that keep AI responses anchored in real genomic and scientific evidence.
• Partner with bioinformatics experts to ensure outputs match the science, and with product/design specialists on user-facing behavior.

Who you are

• 5+ years building and operating production backend systems at scale.
• Expert-level in TypeScript, comfortable owning production services end to end.
• Strong distributed-systems fundamentals - you understand how they're designed and why they fail.
• Hands-on with large-scale data processing frameworks (Apache Spark or equivalent) and very large datasets.
• Deeply familiar with both OLTP and OLAP data systems (PostgreSQL, DuckDB)
• Solid with distributed event-driven systems.
• Able to step into an unfamiliar domain like genomics, learn the mechanics fast, and go deep.
• Craft-driven: you build systems properly with attention to detail and high bar for quality rather than assembling pre-made pieces.
• This is a fully remote role open to candidates in time zones from UTC−5 to UTC+3.

Bonus to have

• Rust, and functional programming experience (Scala or similar).
• Python for data processing.
• Experience training or fine-tuning your own AI models - not just calling APIs.
• Experience with multi-agent AI systems and orchestration (planner / router / evaluator patterns).
• Production experience with LLM APIs (Anthropic, OpenAI, Bedrock, Google AI) 
• Production experience with vector databases and RAG pipelines.
• LLM observability tooling (Langfuse, LangSmith).
• Workflow engines (Temporal).
• Familiarity with genomics, bioinformatics, or health data systems.
• High-growth startup experience.

Why this role matters