Motion Recruitment
Machine Learning Engineer / Voice-centric AI
Motion Recruitment, Chicago, Illinois, United States, 60290
We're looking for an entrepreneurial
Senior Machine Learning Engineer
with experience taking voice-centric AI systems (TTS, STT, LLM-driven dialog) from prototype to large-scale production. You'll own the full ML lifecycle-research, data pipelines, training, evaluation, deployment, and ongoing optimization-powering sub-second, natural voice conversations at scale.
This role is ideal for someone passionate about
pushing the limits of conversational AI : creating highly optimized, domain-specific models that are faster, leaner, and more cost-efficient than general-purpose solutions. You'll collaborate closely with product, infrastructure, and compliance teams, while setting the technical bar for model excellence and ML best practices.
Required Skills & Experience Experience: 7+ years building production ML systems, including 2+ years in speech or conversational AI. Proven track record deploying large-scale voice AI or LLM products. Fine-tuning & compression (LoRA, QLoRA, quantization, pruning, distillation). Speech (ASR: Whisper, NeMo, Kaldi; TTS: Tacotron, FastSpeech, VITS). LLMs & dialogue (GPT-class, RAG, LangGraph, LangChain, MCP). Strong in Python; bonus for TypeScript/Node/Java. Infra & Ops (Kubernetes, Helm, Terraform, MLflow/SageMaker). Data systems (Kafka, Redis, Postgres, Snowflake). Streaming protocols (gRPC, WebSockets, HTTP/2, WebRTC). Security & compliance (HIPAA, SOC2, HITRUST). Product-oriented, entrepreneurial, strong problem solver, effective communicator, and technical leader. Desired Skills & Experience
Optimize & Fine-Tune Models: Apply LoRA, QLoRA, RLHF, and other parameter-efficient techniques. Use quantization, pruning, and distillation to shrink models while preserving quality. Build End-to-End Pipelines: Design STT, TTS, and LLM systems achieving Scale Inference: Optimize serving on Kubernetes/EKS with dynamic batching, speculative decoding, and streaming protocols. Advance Dialogue Management: Extend LangGraph/LangChain flows and MCP schemas for complex multi-turn conversations. Data & Evaluation: Develop pipelines for conversational logs (Kafka ? Snowflake/S3) and create frameworks to measure ASR accuracy, TTS quality, and task completion. Lead & Mentor: Define ML best practices, champion model CICD and monitoring, and mentor teammates on ML Ops, speech processing, and prompt engineering. Innovate & Research: Run POCs with cutting-edge models (e.g., Whisper-v3, Bark) and stay ahead of the latest in speech + LLM research. Ensure Reliability & Compliance: Implement HIPAA-grade security, PHI safeguards, and robust fallback strategies. The Offer
Bonus eligible You will receive the following benefits:
Medical, Dental, and Vision Insurance Vacation Time Stock Options Applicants must be currently authorized to work in the US on a full-time basis now and in the future.
#LI-EM1
Posted by:
Esteban Medina
Specialization :
Machine Learning/Data Science
Senior Machine Learning Engineer
with experience taking voice-centric AI systems (TTS, STT, LLM-driven dialog) from prototype to large-scale production. You'll own the full ML lifecycle-research, data pipelines, training, evaluation, deployment, and ongoing optimization-powering sub-second, natural voice conversations at scale.
This role is ideal for someone passionate about
pushing the limits of conversational AI : creating highly optimized, domain-specific models that are faster, leaner, and more cost-efficient than general-purpose solutions. You'll collaborate closely with product, infrastructure, and compliance teams, while setting the technical bar for model excellence and ML best practices.
Required Skills & Experience Experience: 7+ years building production ML systems, including 2+ years in speech or conversational AI. Proven track record deploying large-scale voice AI or LLM products. Fine-tuning & compression (LoRA, QLoRA, quantization, pruning, distillation). Speech (ASR: Whisper, NeMo, Kaldi; TTS: Tacotron, FastSpeech, VITS). LLMs & dialogue (GPT-class, RAG, LangGraph, LangChain, MCP). Strong in Python; bonus for TypeScript/Node/Java. Infra & Ops (Kubernetes, Helm, Terraform, MLflow/SageMaker). Data systems (Kafka, Redis, Postgres, Snowflake). Streaming protocols (gRPC, WebSockets, HTTP/2, WebRTC). Security & compliance (HIPAA, SOC2, HITRUST). Product-oriented, entrepreneurial, strong problem solver, effective communicator, and technical leader. Desired Skills & Experience
Optimize & Fine-Tune Models: Apply LoRA, QLoRA, RLHF, and other parameter-efficient techniques. Use quantization, pruning, and distillation to shrink models while preserving quality. Build End-to-End Pipelines: Design STT, TTS, and LLM systems achieving Scale Inference: Optimize serving on Kubernetes/EKS with dynamic batching, speculative decoding, and streaming protocols. Advance Dialogue Management: Extend LangGraph/LangChain flows and MCP schemas for complex multi-turn conversations. Data & Evaluation: Develop pipelines for conversational logs (Kafka ? Snowflake/S3) and create frameworks to measure ASR accuracy, TTS quality, and task completion. Lead & Mentor: Define ML best practices, champion model CICD and monitoring, and mentor teammates on ML Ops, speech processing, and prompt engineering. Innovate & Research: Run POCs with cutting-edge models (e.g., Whisper-v3, Bark) and stay ahead of the latest in speech + LLM research. Ensure Reliability & Compliance: Implement HIPAA-grade security, PHI safeguards, and robust fallback strategies. The Offer
Bonus eligible You will receive the following benefits:
Medical, Dental, and Vision Insurance Vacation Time Stock Options Applicants must be currently authorized to work in the US on a full-time basis now and in the future.
#LI-EM1
Posted by:
Esteban Medina
Specialization :
Machine Learning/Data Science