ML Engineer – Signal Processing, ASR

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🕒 April 29

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Fresh Prints

WebsiteLinkedInAll Job Openings

11 - 50 employees

Founded 2013

🛒 Retail

🛍️ eCommerce

👥 B2C

Retail • eCommerce • B2C

Fresh Prints is a company specializing in custom collegiate merchandise. They offer a wide range of apparel including hoodies, shirts, sweaters, and sweatpants, catering primarily to college students and organizations such as fraternities and sororities. Customers can customize their garments using Fresh Prints' design tool and upload their own inspirations for a personalized touch. The company operates online and is based out of New York City. They are actively engaged with their community through social media and an interactive blog, and they provide opportunities for students to become campus managers, enhancing their presence in the collegiate community.

📋 Description

• Design, build, and improve ASR, audio, and speech-related ML systems for production. • Develop signal processing pipelines for noisy, compressed, telephony-style, or real-world audio. • Train, fine-tune, evaluate, and deploy models for ASR, audio classification, diarization, redaction, or related tasks. • Own ML workflows end-to-end: data preparation, model training, validation, inference, monitoring, and iteration. • Optimize inference for latency, throughput, cost, and reliability. • Debug model quality issues through data analysis, targeted evaluations, and production monitoring. • Collaborate with product and engineering teams to turn business problems into practical ML solutions.

🎯 Requirements

• At least 5 years of hands-on experience deploying ASR or other ML systems in production. • Strong background in signal processing, speech recognition, audio ML, or telephony/audio pipelines. • Experience with production ASR systems, streaming inference, VAD, noise handling, diarization, speaker/channel issues, or similar speech technologies. • Strong Python engineering skills and experience building production services. • Experience with frameworks such as PyTorch, TensorFlow, JAX, ONNX Runtime, or similar. • Experience deploying models with Docker, Kubernetes, FastAPI, Triton, vLLM, TorchServe, custom inference services, or cloud ML platforms. • Strong understanding of model evaluation, regression testing, observability, latency, memory, GPU/CPU utilization, and cost-performance tradeoffs. • Comfort working with messy real-world data, noisy labels, domain drift, and ambiguous production issues.

🏖️ Benefits

• Medical insurance • Professional development opportunities • Flexible work hours • Remote work options