Principal Software Engineer
Level AI
Principal Software Engineer
Level AI
Optimizes call center data with AI
Level AI is a Series C conversational intelligence company headquartered in Mountain View, CA, backed by top-tier VCs and Silicon Valley operators. We help enterprise contact centers understand every customer conversation — using speech AI, NLP/NLU, and retrieval systems to turn millions of unstructured interactions into decisions businesses can act on.
We're at the scaling inflection point. The systems that carried us from Series A to Series C won't carry us to the next stage, and we need someone to own that transition — not just build inside it.
This is a Principal role: you'll set technical direction for backend and ML infrastructure across multiple teams, make the architectural calls that are expensive to reverse, and raise the engineering bar through design reviews, mentorship, and the standards you set by example. You'll report to the VP of Engineering and partner directly with our ML, Product, and Infrastructure leads across both sites.
You'll work alongside engineers from Amazon, Google, and Meta who chose to build here because the problems are unsolved and the ownership is real.
Own the architecture for real-time data processing at scale. Design and evolve distributed messaging systems that handle high-throughput streaming workloads with strict latency requirements.
Build the ML platform that lets us ship models faster. Define and execute the technical roadmap for training and serving infrastructure as our models grow in size, complexity, and inference cost.
Scale our GPU infrastructure. Own capacity planning, scheduling, and utilization across training and inference fleets — deciding what runs where, how we handle burst demand, and how we keep spend tied to actual throughput rather than idle reservations.
Scale inference. Drive down latency and cost per request as model complexity and traffic grow: batching and routing strategies, quantization and compilation, autoscaling, and caching — without degrading output quality.
Make reliability a property of the system, not a heroic effort. Drive uptime, observability, and incident response for serving systems that enterprise customers depend on in production.
Turn ambiguous business problems into executable technical plans. Partner with Product and GTM to scope large cross-functional initiatives, then break them into work other teams can run with.
Multiply the team. Lead design reviews, mentor senior engineers, and shape the engineering practices that outlast any single project.
Bring the outside in. Evaluate emerging tools and techniques with judgment — adopt what earns its complexity, skip what doesn't.
90 days: You've mapped our critical paths and failure modes, shipped a meaningful improvement to a serving or pipeline bottleneck, and earned trust across both sites.
6 months: You own a published technical roadmap for [messaging / ML infra], with at least one major migration or redesign underway. GPU utilization and inference cost per request are measured, and trending the right way.
12 months: Our infrastructure scales predictably with customer growth, inference cost stays flat or falls as volume rises, on-call load is down, and other engineers are making better architectural decisions because of standards you established.
[10]+ years building backend and infrastructure systems, with a track record of owning architecture and design at scale — not just implementing it.
Deep, hands-on experience with large-scale databases, high-throughput messaging systems, and real-time job queues.
Proven ability to navigate large, complex codebases and reason clearly about architectural tradeoffs in systems you didn't build.
Experience mentoring senior engineers and driving technical decisions through influence rather than authority.
Strong written communication — you'll be making technical cases to engineers in two time zones and business cases to executives.
BTech/MTech/PhD in Computer Science or equivalent. At this level we weight track record well above pedigree.
Production experience with our stack: Django, Celery, Redis, PostgreSQL, and Google Cloud.
Hands-on experience scaling GPU infrastructure and model inference in production — capacity planning, scheduling and utilization, autoscaling, and latency/cost optimization under real traffic.
Depth in specific inference tooling — vLLM, TensorRT, Triton, Ray Serve, or equivalents — and experience benchmarking tradeoffs between them.
Experience scaling a platform through a comparable growth stage (Series C → D, or equivalent), especially at a global product company's India site.
Background in speech, NLP, or information retrieval systems.
Health coverage for you, your spouse, children, and parents.
[home-office and connectivity allowance / annual learning budget / parental leave — trim to what's accurate].
Real ownership over systems used by enterprises worldwide, with the autonomy to decide how they're built.
[4] stages, typically [2] weeks end to end: recruiter screen → technical deep dive → system design → team and leadership conversations → offer. Interviews are conducted from our India team with [one/two] conversations with US-based leadership. We'll tell you where you stand at every step.
Level AI is an equal opportunity employer. We hire and promote on merit and evaluate all applicants without regard to caste, religion, sex, gender identity, sexual orientation, marital or parental status, disability, or any other personal characteristic unrelated to the job. We maintain a zero-tolerance policy on harassment in line with the POSH Act, 2013. If you need any accommodation during the interview process, tell us — we'll arrange it.
Level AI provides software that improves customer experience and operational efficiency in the BPO sector by integrating human and machine intelligence. Its platform unifies call center data from multiple sources and uses intelligent automation and machine learning to automate tasks, guide decisions, and improve interactions, all delivered through a subscription model. It differentiates itself by offering end-to-end data integration across silos and blending human expertise with ML to boost sales conversions and QA productivity in call centers. The goal is to help BPO providers deliver better customer experiences while making operations more efficient and cost-effective.
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