What You’ll Do Dive into model architectures (ASR / TTS / SLMs) and optimize them for specific GPUs and hardware profiles Build, debug, and tune kernels using CUDA / Tinygrad / AMD toolchains Convert, optimize, and benchmark models using TensorRT, ONNX, and other inference engines Work hands-on with PyTorch to train, fine-tune, and evaluate real-time speech models Run large-scale experiments, manage datasets, and analyze model performance at scale Productionize models for ultra-low latency speech workloads Collaborate with research, infra, and product teams to push models into production Requirements Strong experience with CUDA, Tinygrad, AMD GPU toolkit, or similar low-level GPU programming stacks Hands-on proficiency with PyTorch and Python Deep understanding of neural networks, training dynamics, and optimization Experience handling and processing large datasets Familiarity with production inference pipelines Strong problem-solving skills with ability to go deep into performance bottlenecks Great to Have Experience training speech models (ASR, TTS, SSL, etc.) Familiarity with audio encoders, decoders, waveform models Experience with MLOps, experiment tracking, deployment pipelines Training or fine-tuning models for production / published papers Experience with TensorRT and ONNX Runtime
About the Role We’re looking for a Tech Support Engineer who loves diving into logs, writing quick scripts, and helping customers win. You’ll sit right at the intersection of engineering + customer success, making sure every issue that comes in gets solved with speed, empathy, and solid code. If you can debug APIs in Next JS, spot a React crash from a mile away, write code in python and still stay calm on a Zoom call with a customer — this one’s for you. What You’ll Do 1. Own customer-facing issues end-to-end — debug, fix, communicate, repeat. 2. Write small tools and scripts in Python / Go / JS to automate and monitor support workflows. 3. Set up and improve support tooling — logs, dashboards, alerts, uptime monitors, whatever keeps things smooth. 4. Jump into AWS / Kubernetes when needed to figure out why something’s on fire. 5. Keep SLA/SLOs in check and make sure no ticket goes dark. 6. Be the calm, technical voice that customers love talking to. 7. Turn every weird bug you solve into clean internal documentation. What You Need to Already Know (what we will love to learn from you) 1. Solid skills in Python, JavaScript/React, and Go (enough to debug and patch things fast). 2. Basic Kubernetes and AWS Cloud awareness — EC2, S3, CloudWatch, etc. 3. Knows your way around HTTP APIs, logs, and networking basics. 4. You’ve used or can quickly pick up tools like Grafana, Sentry, Postman, NewRelic or Datadog. 5. Exposure to support products like Intercom, Pylon, Zendesk, Freshdesk, Zoho & so on. 6. Great communication — can you explain the same things to Engineering and Sales ?? Nice-to-Haves (What you can learn just by being in office with us) 1. Curious about deep learning / voice-AI / model inference. 2. You’ve played with PyTorch, Hugging Face, or real-time audio systems. 3. Love for building internal automations or dashboards. 4. Prior experience in B2B SaaS / AI / infra startups. Why You’ll Love It Here 1. You’ll work directly with our engineers, founders, and customers — no silos, no red tape. 2. You’ll touch everything from APIs infra AI models . 3. Your fixes will literally keep enterprise voice-AI systems alive. 4. You’ll grow fast into DevOps, Platform Engg, or ML Infra if you want. 5. We move fast, break smart, and ship often with crazy ownership - only join if you aspire to be the best in the world. Sound like your vibe? Drop us a note — and a story about the hardest bug you’ve ever squashed.