Senior Backend Engineer - Speech Platform

About role: You'll be the engineering owner on the speech team, working alongside two ML engineers and building everything around the models — ingestion, training infrastructure, real-time serving, and the integration into our existing platform. This is a backend engineering role. You don't need to train models. You need to build the systems that make trained models useful in production.

Responsibilities

Build the audio data pipeline: mine our call archive, transcode, resample, segment, deduplicate and quality-filter at scale Build and operate real-time inference services with hard latency targets, including streaming, cancellation and mid-utterance interruption Integrate speech services into our existing Java-based platform and telephony infrastructure Instrument the full latency budget end to end and find where the milliseconds go Stand up training infrastructure — GPU scheduling, checkpointing, experiment tracking, reproducibility Own compute cost and concurrency economics: how many simultaneous calls per GPU, and how to improve it Support on-premise deployment for clients who can't send data outside their network Requirements Must haves: 3–4 years building and operating production backend systems Strong Java — you've owned services in production, not just contributed to them Working Python — enough to build data pipelines and integrate with ML tooling Real-time or low-latency systems experience: streaming APIs, Web.

Socket or gRPC streaming, concurrency, backpressure Data pipelines at scale (Airflow, Dagster, Spark or equivalent) Docker and Kubernetes in production Cloud infrastructure (AWS/GCP/Azure) Comfortable debugging performance: profiling, latency percentiles, throughput under load Nice to have: Serving ML models in production (Triton, vLLM, Torch.

Serve) Audio tooling — ffmpeg, sox, codecs, resampling Telephony — SIP, Asterisk/FreeSWITCH, media servers, narrowband codecs MLOps tooling: MLflow, Weights & Biases, DVC GPU-aware infrastructure work