Senior AI & DSP Audio Engineer / Senior Machine Learning Engineer

Remote | Full-Time | U.S. Working Hours | Fluent English Required

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

Location: Remote (collaboration aligned with U.S. business hours)

Apply: Send your English resume to jobs+Data709@azcare.ai

AZcare is building the first enterprise-grade, HIPAA-compliant outbound AI calling platform for healthcare. Our AI agents autonomously handle high-friction, phone-based workflows that consume billions of hours annually, including:

• Medical appointment scheduling and follow-ups
• Insurance eligibility and billing inquiries
• Hospital and care coordination workflows
• Readmission prevention and patient outreach

We operate at the intersection of applied machine learning, real-world audio systems, and regulated healthcare environments. Our work is production-driven, reliability-focused, and designed to create measurable impact for patients, providers, and employers.

We are seeking a Senior AI & DSP Audio Engineer / Senior Machine Learning Engineer to lead and evolve the intelligent audio and ML systems powering AZcare’s voice-driven platform.

This is a hands-on, end-to-end role covering DSP algorithm design, audio modeling, data pipelines, machine learning, and production deployment. You will work closely with engineering, product, and operations to translate research-grade ideas into robust, real-world systems.

This role is well-suited for an experienced engineer who is comfortable owning complex systems and operating in ambiguity typical of early-stage, high-impact products.

• Design, implement, and optimize digital signal processing (DSP) and audio analysis pipelines
• Model real-world acoustics, microphone behavior, noise, and speech variability
• Apply machine learning to audio, speech, and related modalities (with optional exposure to vision)

• Collect, extract, and structure data from online and offline sources
• Clean, label, and transform raw data into production-quality datasets
• Build scalable preprocessing and feature-engineering pipelines

• Train, fine-tune, and optimize ML models for reliability, latency, and robustness
• Adapt pre-trained models to domain-specific healthcare workflows
• Deploy models into production systems with monitoring and iteration loops

• Perform prompt engineering and workflow design for LLMs (e.g., GPT-class models)
• Apply LLMs to summarization, automation, analytics, and operational decision support
• Fine-tune or adapt LLMs for healthcare-specific and compliance-sensitive use cases

• Define meaningful performance metrics beyond offline accuracy
• Debug failure cases and edge conditions typical of real-world audio systems
• Continuously improve system accuracy, stability, and scalability

• Partner with engineering, product, and operations stakeholders
• Communicate technical trade-offs clearly and pragmatically
• Contribute to architectural decisions and long-term technical strategy

• M.S. with 5+ years of industry experience or B.S. with 10+ years of industry experience
• Strong proficiency in Python
• Hands-on experience with PyTorch, TensorFlow, or Scikit-learn
• Experience with data extraction and web scraping (e.g., BeautifulSoup, Scrapy, APIs)
• Deep understanding of data cleaning, preprocessing, and feature engineering
• Familiarity with cloud platforms (AWS, GCP, or Azure) for ML deployment
• Strong written and verbal English communication skills
• Proven ability to operate independently and solve open-ended problems

• Experience with audio ML, speech processing, ASR, VAD, or conversational systems
• Familiarity with vector databases, LLM orchestration, or agent frameworks
• Prior experience deploying ML systems into production at scale
• Exposure to regulated or compliance-constrained environments (e.g., healthcare, finance)

• Work on a real, unsolved problem: eliminating phone-based friction in healthcare
• Build systems that operate in messy, real-world conditions, not demos
• Collaborate with a senior, globally distributed, execution-focused team
• High ownership, high trust, and direct impact on product direction
• Opportunity for long-term growth as the platform scales

If you would like, I can also:

• Make this more research-heavy or more production-focused
• Adjust compensation framing for specific geographies
• Optimize for LinkedIn, Wellfound, or Upwork
• Flag wording that may raise compliance or classification concerns

Just tell me how you plan to post it.