Senior AI & DSP Audio Engineer / Machine Learning Engineer

Remote | 🕒 U.S. Working Hours | 🗣️ Fluent English Required

Compensation: $800 – $2,000 USD per month (commensurate with skills and experience)

Experience:

• M.S. + 5+ years industry experience OR
• B.S. + 10+ years industry experience (required)

To Apply: Send your English resume to [email protected]

AZcare is building the first enterprise-grade, HIPAA-compliant outbound AI calling agent for healthcare. Our platform automates high-impact workflows such as:

• Medical appointment scheduling
• Insurance billing and eligibility inquiries
• HR communications
• Hospital readmission prevention

We combine cutting-edge machine learning, intelligent audio processing, and deep healthcare expertise to reduce administrative burden and improve patient outcomes. As a fast-growing health-tech startup, our work directly impacts millions of people—and we’re just getting started.

We are seeking a highly experienced AI & DSP Audio Engineer / Senior Machine Learning Engineer to own and advance the intelligent audio and ML systems behind AZcare’s voice-driven healthcare platform.

This is a full-time, remote role requiring strong English communication skills and close collaboration with engineering, product, and operations teams. You will work end-to-end—from data acquisition and DSP algorithm design to production-grade ML deployment.

• Design, develop, and optimize digital signal processing (DSP) and audio analysis algorithms
• Build and own end-to-end machine learning pipelines, from data ingestion to production deployment
• Tune audio systems, optimize microphones, and model acoustics for real-world environments
• Apply ML to audio, speech, and related modalities (with potential exposure to computer vision)

• Collect, scrape, and extract structured and unstructured data from online and offline sources
• Clean, label, and transform raw data into high-quality training datasets
• Build scalable preprocessing and feature-engineering pipelines

• Train, fine-tune, and optimize ML models for real-world performance
• Customize pre-trained models for domain-specific healthcare use cases
• Integrate models into production systems with reliability and scalability in mind

• Perform prompt engineering for models such as GPT, PaLM, or similar
• Apply LLMs for summarization, automation, analytics, and workflow optimization
• Fine-tune or adapt LLMs for healthcare-specific and operational applications

• Define and track appropriate performance metrics
• Debug, iterate, and refine models to meet production-grade reliability standards
• Continuously improve system accuracy, latency, and robustness

• Partner closely with engineering, product, and operations teams
• Translate complex technical insights into actionable solutions
• Contribute to technical strategy, architecture decisions, and roadmap planning

• M.S. with 5+ years of industry experience OR B.S. with 10+ years of industry experience
• Strong programming skills in Python
• Hands-on experience with PyTorch, TensorFlow, or Scikit-learn
• Experience with web scraping (e.g., BeautifulSoup, Scrapy) and APIs
• Deep understanding of data cleaning, preprocessing, and feature engineering
• Familiarity with AWS, GCP, or Azure for ML deployment
• Excellent written and verbal communication skills
• Strong problem-solving ability and a growth-oriented mindset

• Experience with audio ML, speech processing, ASR, or VAD
• Familiarity with vector databases, LLM orchestration, or agent frameworks
• Experience deploying ML models into production environments

• Help solve the “on-hold crisis” in healthcare through intelligent automation
• Work with a mission-driven, globally distributed team
• Build systems that deliver immediate, real-world impact
• Gain access to mentorship, career growth, and professional development

If you’d like, I can also:

• Make this more senior-focused or more hands-on/IC-focused
• Adjust tone for LinkedIn, Wellfound, or Upwork
• Optimize it for international candidates or U.S.-based talent
• Flag anything that may raise compensation or compliance questions