Backend Engineer AI Infrastructure

New York City, NY - USA

Monthly Salary: Not Disclosed

Posted on: 8 hours ago

Vacancies: 1 Vacancy

Job Summary

Arbol is a global climate risk coverage platform and FinTech company offering full-service solutions for any business looking to analyze and mitigate exposure to climate risk. Arbols products offer parametric coverage which pays out based on objective data triggers rather than subjective assessment of loss. Arbols key differentiator versus traditional InsurTech or climate analytics platforms is the complete ecosystem it has built to address climate risk. This ecosystem includes a massive climate data infrastructure scalable product development automated instant pricing using an artificial intelligence underwriter blockchain-powered operational efficiencies and non-traditional risk capacity bringing capital from non-insurance sources. By combining all these factors Arbol brings scale transparency and efficiency to parametric coverage.

Arbol is looking for a backend engineer to join the team to help build the data infrastructure for our production AI this role youll own the full lifecycle of our production LLM applications: ingestion transformation storage retrieval evaluation and monitoring. The ideal candidate has a strong quantitative background in math statistics or computer science. This is a hands-on role for an engineer thats comfortable owning systems end-to-end and can move quickly without sacrificing quality.

What Youll Be Doing

Build pipelines for structured and unstructured data including docs PDFs emails and logs
Design and implement backend services that support LLM workflows: retrieval indexing embeddings batch processing and job queues
Own data quality end to end: validation deduping lineage versioning and reproducible runs
Create observability for pipelines and LLM systems to surface issues quickly and identify root causes clearly

What Youll Need

Solid backend engineering skills in Python
Strong understanding of data systems: SQL schemas indexing partitioning and performance tradeoffs
Comfort with statistical concepts relevant to model evaluation or experience interpreting metrics and distributions

Whats Great to Have

Familiarity with pipeline tooling (Airflow/Dagster/Prefect) queues (Kafka/SQS/PubSub) and common data formats (Parquet/JSON)
Comfort with cloud infrastructure (AWS/GCP/Azure) containers CI/CD and basic DevOps hygiene
Experience with LLM support systems (vector DBs embeddings RAG patterns eval frameworks) and/or distributed compute (Spark/Ray)

$120000 - $150000 a year

Candidates for this role must be located within the United States.

Essential Job Functions & Physical Requirements

Ability to sit for extended periods of time while working at a computer with or without reasonable accommodation

Ability to use a computer keyboard mouse and standard office equipment (e.g. phone printer scanner)

Ability to view a computer screen for prolonged periods with or without reasonable accommodation

Ability to communicate effectively in person by phone and via email

Ability to occasionally stand walk bend and reach within an office environment

Ability to lift and/or move up to 1015 pounds occasionally (e.g. office supplies files) with or without reasonable accommodation

Ability to perform repetitive motions such as typing or data entry

Ability to maintain focus and attention while performing detailed tasks

Interested but you dont meet every qualification Please apply!

Arbol values the perspectives and experience of candidates with non-traditional backgrounds and we encourage you to apply even if you do not meet every requirement.

Accessibility

Arbol is committed to accessibility and inclusivity in the hiring process. As part of this commitment we strive to provide reasonable accommodations for persons with disabilities to enable them to access the hiring process. If you require an accommodation to apply or interview please contact emailprotected

Benefits

Arbol is proud to offer its full-time employees competitive compensation and equity in a high-growth startup. Our health benefits include comprehensive health dental and vision coverage and an optional flexible spending account (FSA) to support your health. We offer a 401(k) match to support your future and flexible PTO for you to relax and recharge.

Equal Opportunity Employer

Arbol is an Equal Opportunity Employer and does not discriminate on the basis of race color religion sex (including pregnancy sexual orientation or gender identity) national origin age disability veteran status or any other legally protected status.

Arbol participates in the E-Verify program to confirm employment eligibility.

We may use artificial intelligence (AI) tools to support parts of the hiring process such as reviewing applications analyzing resumes or assessing responses. These tools assist our recruitment team but do not replace human judgment. Final hiring decisions are ultimately made by humans. If you would like more information about how your data is processed please contact us.

Required Experience:

What Youll Be Doing

Build pipelines for structured and unstructured data including docs PDFs emails and logs
Design and implement backend services that support LLM workflows: retrieval indexing embeddings batch processing and job queues
Own data quality end to end: validation deduping lineage versioning and reproducible runs
Create observability for pipelines and LLM systems to surface issues quickly and identify root causes clearly

What Youll Need

Solid backend engineering skills in Python
Strong understanding of data systems: SQL schemas indexing partitioning and performance tradeoffs
Comfort with statistical concepts relevant to model evaluation or experience interpreting metrics and distributions

Whats Great to Have

Familiarity with pipeline tooling (Airflow/Dagster/Prefect) queues (Kafka/SQS/PubSub) and common data formats (Parquet/JSON)
Comfort with cloud infrastructure (AWS/GCP/Azure) containers CI/CD and basic DevOps hygiene
Experience with LLM support systems (vector DBs embeddings RAG patterns eval frameworks) and/or distributed compute (Spark/Ray)