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About Aira We build AI agents that run cellular networks. Mobile operators spent billions on 5G but can't extract value—vendor lock-in blocks innovation, monolithic systems don't scale, operational complexity multiplies. We deploy multi-agent systems with fine-tuned LLMs that autonomously optimize network performance, generate operational tools, and execute operator intent. Applied AI at planetary scale: millions of towers, petabytes of telemetry, real-time inference affecting billions of users. Founded by Qualcomm and Intel veterans. Team from Stanford, Berkeley, Cornell. Backed by AT&T Ventures, Intel Capital, In-Q-Tel, NeoTribe, Acrew, and more. The Role Build the data backbone that makes autonomous networks possible. You'll transform raw signals from millions of cell towers into the intelligence layer our AI agents use to optimize in real-time - streaming pipelines that feed agent decision-making, models that power operator dashboards, feature stores that enable inference at scale. Your work directly determines what our agents can see and how fast they can respond. You'll collaborate directly with ML researchers tuning models on your data, wireless engineers defining what matters, and product teams shipping analytics to tier-1 operators. Ship fast, iterate, own the full stack from ingestion to insight. What You'll Build Config-driven ingestion from multi-vendor RAN element managers into cloud object storage and Snowflake - bronze through gold A modeled KPI layer at cell, carrier, and market granularity that agents and dashboards both query, with contracts stable enough to build on Serving APIs over the gold layer - typed, paginated, cached, and fast enough for interactive queries at cell granularity, consumed by both the UI and our agents Data quality, freshness SLAs, lineage, observability, and cost controls for datasets a tier-1 NOC depends on What We're Looking For 5+ years building production systems with data at the center of them Deep SQL, plus real understanding of query planning, partitioning, and warehouse cost/performance tradeoffs Strong Python; production experience with Spark and workflow orchestration (Airflow, Dagster, or similar) Backend API experience (FastAPI, Flask, or equivalent) serving data to applications and models Time-series or event data at scale: schema evolution, late arrivals, idempotency, backfill strategy Bonus: telecom/RAN data (3GPP NRM, PM/CM/FM), dbt, Iceberg/Delta, or building serving layers that LLM agents query No telecom background required - we'll teach you the domain; we can't teach you judgment about production data. Why Aira Direct access to founders, partners and researchers Work on multi-agent systems and fine-tuned LLMs in production before most companies figure out prompts Partner with tier-1 mobile operators globally High autonomy, real ownership, visible impact We're building the intelligent network. Help us ship it.
Here's how to pick the right one and stand out in your application.
144.883Jobs
31.687IN
81%EN
That number is real. WorkMundi's database shows 144,883 open engineer roles across the world. India has the most with 31,687 jobs, followed by the United States with 30,084. If you just finished reading one job ad and felt paralyzed by choice, you're not alone—but this scale is actually an advantage. It means you can afford to be selective.
Start by geography and language. The majority of engineer ads—117,837 of them—have the job posting text written in English. Use that as one filter, but remember: the ad text language tells you nothing about whether the role actually requires you to speak English day-to-day. Read the job description carefully. Then check which countries have the volume you're targeting. Singapore, Poland, and Australia round out the top five after India and the US.
Next, learn who's hiring. Accenture has posted 2,801 engineer roles. andurilindustries, speechify, and jobgether are also actively recruiting. If you're applying to one of these names, research their hiring patterns and interview style before you apply. That homework pays off.
When you interview, expect the question every engineer hears: 'Tell me about a time you had to debug a problem that wasn't in your job description.' Have a specific story ready—not a general one. Name the tools, the deadline pressure, and what you learned. Hiring managers listen for whether you see problem-solving as part of the role itself, not a favour.