Software Engineer, Infrastructure (ML and Real-Time Speech)

Otter.ai•Mountain View, CA

10d

About The Position

We’re looking for an experienced Software Engineer to join a team that empowers you to grow, be your authentic self, and do your best work every day. At Otter, we’ve built a platform that simplifies note taking, saves time, improves productivity and accessibility, and makes collaboration effortless. If this mission resonates with you, let’s talk. We need your help building the infrastructure that powers some of the most advanced multi-language, automatic speech recognition (ASR) and real-time speaker identification systems on the market. You’ll design and scale the backend platforms that bring cutting-edge speech AI to life, working shoulder-to-shoulder with our machine learning researchers and product managers to deliver resilient, high-performance systems that enable real-time, multilingual AI experiences at scale. If you’re passionate about building rock-solid infrastructure for AI-driven products, enjoy solving complex distributed-systems problems, and want your work to have an immediate impact on millions of users, this is your opportunity.

Requirements

2 + years of experience designing and building scalable backend systems and distributed infrastructure.
Is fluent in Python (bonus for C++) and familiar with building high-performance services.
Understands data structures, algorithms, distributed systems, and operating systems at a deep level.
Has worked with databases (SQL & NoSQL), queuing systems, and pub/sub solutions, and knows what’s happening “under the hood.”
Has experience with ML infrastructure / MLOps (e.g., model deployment, feature stores, pipelines) or is eager to learn quickly.
Enjoys collaborating with ML engineers and product teams to translate research into production-ready systems.
Thrives in a fast-paced environment where innovation and reliability go hand in hand.

Nice To Haves

Experience with real-time streaming frameworks (Kafka, Pulsar, Flink).
Hands-on experience with container orchestration (Kubernetes, ECS).
Familiarity with SDK integrations or developer platform design.
Experience building or optimizing speech/voice AI pipelines.

Responsibilities

Design, build, and operate backend services that power real-time, multi-language, ASR and speaker identification features.
Architect scalable, fault-tolerant infrastructure across databases, queues, and pub/sub systems to support low-latency ML workflows.
Partner with ML teams to productionize models (training pipelines, model deployment, versioning, and monitoring) for real-time and batch inference.
Improve system observability, reliability, and performance for large-scale speech data ingestion and streaming.
Contribute to the evolution of our platform architecture, including microservices, orchestration (Kubernetes), and API integrations.
Collaborate with cross-functional teams to streamline developer experience, CI/CD pipelines, and automated testing for ML-backed services.

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