Machine Learning Engineer

*About Cogni:*

Cogni is a brain health lab turning voice biomarker research into real-world tools for early detection and cognitive monitoring. We build products that analyze natural speech to detect and classify cognitive disorders, including early-stage dementia. We're a small team working out of the University of Waterloo Velocity Incubator, a CES 2026 Innovation Award winner, and backed by Baycrest Hospital, Canada's global leader in aging and brain health research.

*The Role:*

You'll work on the ML systems at the core of our platform, building and improving models that extract insights from voice data. Full pipeline ownership, from research to production.

*What you'll do:*

• Design, train, and iterate on ML models for voice biomarker analysis (speech features, NLP, multimodal signal fusion)

• Build production-grade inference pipelines that run in real time

• Collaborate with clinical researchers on validation studies and translate findings into model improvements

• Experiment with cutting-edge voice AI architectures and embeddings to push accuracy benchmarks

• Help make build-vs-buy decisions on compute, tooling, and infrastructure

*Must-haves:*

• 2+ years building and deploying ML models (co-ops and capstone projects count)

• Strong Python and fluency with ML frameworks (PyTorch, TensorFlow, scikit-learn, etc.)

• Experience with audio/speech processing, NLP, or time-series data

• Comfort with the full ML lifecycle: data wrangling, feature engineering, training, evaluation, deployment

*Nice-to-haves:*

• Experience with real-time inference, streaming, or voice AI systems (Pipecat, Deepgram, Whisper)

• Familiarity with clinical/health ML workflows or REB-approved studies

• GPU infrastructure experience (CUDA, containerized workloads, cloud or on-prem)

• Published research or open-source contributions in speech/audio ML