Interfere

19 hours ago

Software Engineer, Data

✓ Verified Job

Canonical indexed version, validated from employer's careers page.

Source: Company careers pageValidated by: Fursa AI

Last checked: 13 hours agoStatus: Live

Job Summary

Build and own the data backbone for detecting product failures in real time. You’ll design high-throughput ingestion, low-latency stream processing, and fast storage/query systems (including ClickHouse) for events, traces, logs, and session data. Partner closely with AI/ML and product teams on pipelines, schemas, and retrieval infrastructure so agents and models can quickly access the right context, while ensuring cost, observability, and reliability at scale.

Location: New York

Workplace: Onsite

Employment Type: Full time

Job Function: Software Engineering

Seniority: Entry level

Key Responsibilities

•Own the data backbone by building, storing, and querying systems that power product reasoning on events, traces, logs, runtime behavior, code, and session data.
•Design high-throughput ingestion capable of handling billions of events without dropping data or driving up infrastructure costs.
•Build real-time stream processing for detection and diagnosis with correctness and low latency.
•Develop storage and query systems that stay fast as customer data grows significantly.
•Create indexing, retrieval, and data-quality systems (schemas/taxonomy) plus the cost, observability, and reliability layer for internal data systems.

Pay and Benefits

Salary: USD 160,000 - 250,000 annually

Equity and Bonus:Equity

Perks:Health InsuranceDentalVisionRelocation

Key Requirements

•Built and operated production data infrastructure at meaningful scale with real throughput and cost pressure.
•Fluent in distributed-systems tradeoffs (streaming vs batch, consistency vs latency, fidelity vs sampling, storage vs compute) and choosing the right approach for the situation.
•Can take an ambiguous data or infrastructure problem, define the next useful step, and ship without waiting for a fully specified plan.
•Treat cost as a feature and design systems that remain affordable as scale increases.
•Communicate pipeline behavior, failure modes, and tradeoffs clearly to engineers, AI researchers, and PMs.

Experience:Production data infrastructureDistributed systemsObservabilityAI/MLStream processing

Skills:Distributed-systems thinkingCommunicationProblem-solvingCost awarenessDecision-making

Tech Stack:ClickHouseKafkaFlinkKinesisMaterializeGoRustPythonTypeScript

Eligibility

Nationality:US National

Work Authorization:Authorization required. Sponsorship not provided.

Company Brief

Interfere

Provides tools to evaluate, monitor, and mitigate bias and unwanted behavior in machine learning models, offering dataset testing, intervention suggestions, and reporting to improve model robustness and fairness for production ML systems across industries.

Industry: AI & Machine Learning

Website

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Interfere

19 hours ago

Software Engineer, Data

✓ Verified Job

Canonical indexed version, validated from employer's careers page.

Source: Company careers pageValidated by: Fursa AI

Last checked: 13 hours agoStatus: Live

Job Summary

Location: New York

Workplace: Onsite

Employment Type: Full time

Job Function: Software Engineering

Seniority: Entry level

Key Responsibilities

•Own the data backbone by building, storing, and querying systems that power product reasoning on events, traces, logs, runtime behavior, code, and session data.
•Design high-throughput ingestion capable of handling billions of events without dropping data or driving up infrastructure costs.
•Build real-time stream processing for detection and diagnosis with correctness and low latency.
•Develop storage and query systems that stay fast as customer data grows significantly.
•Create indexing, retrieval, and data-quality systems (schemas/taxonomy) plus the cost, observability, and reliability layer for internal data systems.

Pay and Benefits

Salary: USD 160,000 - 250,000 annually

Equity and Bonus:Equity

Perks:Health InsuranceDentalVisionRelocation

Key Requirements

•Built and operated production data infrastructure at meaningful scale with real throughput and cost pressure.
•Fluent in distributed-systems tradeoffs (streaming vs batch, consistency vs latency, fidelity vs sampling, storage vs compute) and choosing the right approach for the situation.
•Can take an ambiguous data or infrastructure problem, define the next useful step, and ship without waiting for a fully specified plan.
•Treat cost as a feature and design systems that remain affordable as scale increases.
•Communicate pipeline behavior, failure modes, and tradeoffs clearly to engineers, AI researchers, and PMs.

Experience:Production data infrastructureDistributed systemsObservabilityAI/MLStream processing

Skills:Distributed-systems thinkingCommunicationProblem-solvingCost awarenessDecision-making

Tech Stack:ClickHouseKafkaFlinkKinesisMaterializeGoRustPythonTypeScript

Eligibility

Nationality:US National

Work Authorization:Authorization required. Sponsorship not provided.

Company Brief

Interfere

Industry: AI & Machine Learning

Website