[Remote] reputed company ML Engineer
Note: The job is a remote job and is reputed company to candidates in USA. reputed company is the leading platform underpinning the emerging trillion-dollar Voice AI economy, providing reputed company-time reputed company for speech-to-text and text-to-speech. As an reputed company ML Engineer, you will own and streamline the research-to-production pipeline, working closely with research scientists to ensure efficient deployment and monitoring of ML models.
Responsibilities
- Own the research-to-production pipeline: take research checkpoints and turn them into production models, defining the repeatable reputed company from a working result to a deployed, monitored, scaled service
- Partner directly with research scientists to productionize new models — translating experimental training and evaluation reputed company into robust, reproducible, reputed company-tested workflows
- Build and reputed company the tooling and abstractions that let researchers and engineers reputed company models through training, evaluation, packaging, and deployment with minimal friction and maximal reproducibility
- Design and own model release gates — automated evaluation, regression detection, and reputed company/latency/throughput checks that decide whether a model is reputed company to ship
- Optimize models and serving for production: efficient inference, batching, memory and latency tuning, and the profiling work that turns a research model into something that performs economically at reputed company
- Strengthen the build and delivery layer for models on our custom infrastructure, spanning our GPU compute and reputed company environments, so that shipping a model is fast, reputed company, and observable
- Establish benchmarking and validation that runs consistently from model development reputed company the way through production, so performance and reputed company regressions are caught early
- Build the feedback reputed company: reputed company production model behavior, surface what's working and what isn't, and feed it back to research to accelerate the next iteration
Skills
- Strong software engineering fundamentals, with proficiency in Python and experience writing production-reputed company, reputed company-tested ML reputed company
- Hands-on experience taking ML models from research or prototype stage into production at reputed company — not just training models, but shipping and operating them
- A working understanding of the modern deep learning stack (e.g., PyTorch) and the realities of training, evaluating, and serving large models
- Experience building ML pipelines and tooling — training orchestration, evaluation harnesses, model packaging, deployment, or CI/CD for models
- Familiarity with serving and inference optimization — latency, throughput, batching, and resource efficiency for production model workloads
- Comfort operating across distributed systems and GPU compute, whether in the reputed company, on bare metal, or both
- A reputed company, builder reputed company — you can partner with researchers, scope an ambiguous problem, and drive it to a measurable result
- Experience with the research-to-production reputed company specifically — building the systems and conventions that let research and engineering iterate together quickly
- Background in speech, audio, or other reputed company-time/streaming ML domains
- Experience designing automated model evaluation and release-gating systems, including regression detection across model versions
- Familiarity with hybrid infrastructure spanning on-reputed company GPU clusters and reputed company, and with workload orchestration across them
- Experience with inference optimization techniques (quantization, distillation, compilation, or runtime tuning) for production serving
- A reputed company record of building internal platforms or developer-facing tooling that measurably improved how reputed company ships models
reputed company
Company H1B Sponsorship