AI that actually understands healthcare is transforming operations, one workflow at a time.
The Role
Lead the development, fine-tuning, deployment, and optimization of machine learning solutions involving LLMs, vision models, and NLP for healthcare applications. Build production-grade models and pipelines, support scalable GPU and TPU training, validate performance and compliance, collaborate with healthcare and product teams, and mentor junior engineers. The role also requires documenting technical approaches and applying MLOps and LLMOps practices in regulated environments.
Summary Generated by Built In
About Autonomize AI
Autonomize AI is revolutionizing healthcare by streamlining knowledge workflows with AI. We reduce administrative burdens and elevate outcomes, empowering professionals to focus on what truly matters — improving lives. We're growing fast and looking for bold, driven teammates to join us.
The Opportunity
As a Senior Machine Learning Engineer at Autonomize, you will lead the development and deployment of machine learning solutions with an emphasis on large language models (LLMs), vision models, and classic NLP (Natural Language Processing) models. The ideal candidate will have a proven track record in these areas, particularly within healthcare contexts, and will play a significant role in advancing our AI-driven healthcare optimized AI Copilots and Agents.
Key Responsibilities
- Help fine-tune or prompt engineer large language models (LLMs) for various healthcare applications across various customer engagements.
- Develop and refine our approach to handling vision based data using state-of-the-art VLM based models capable of processing and analyzing medical documents, healthcare forms in various formats and other visual data accurately.
- Create and enhance classic NLP models to understand and generate human language in healthcare settings, supporting clinical documentation, and patient interaction.
- Collaborate with multi-disciplinary teams including data scientists,ml engineers, healthcare clients, and product managers to deliver robust solutions.
- Ensure models are efficiently deployed and integrated into healthcare systems, maintaining high performance and scalability.
- Mentor and provide guidance to junior engineers and data scientists, fostering a culture of continuous learning and innovation.
- Conduct rigorous testing, validation, and tuning of models to ensure accuracy, reliability, and compliance with healthcare standards.
- Deep understanding of various training techniques including distributed training on GPUs and TPUs.
- Stay informed on the latest research, tools, and technologies in machine learning, particularly those applicable to language and vision processing in healthcare.
- Document methodologies, model architectures, and project outcomes effectively for both technical and non-technical audiences.
Qualifications
- Bachelor's or Master's degree in Computer Science, Engineering, Data Science, or a related field.
- 5-7 years of experience in machine learning engineering, with a significant track record in the developing production grade models and model pipelines in a regulated industry such as healthcare.
- Hands-on expertise in working with large language models (e.g., GPT, BERT), computer vision models, and classic NLP technologies.
- Proficient in programming languages such as Python, with extensive experience in ML libraries/frameworks like TensorFlow, PyTorch, OpenCV, etc.
- Strong understanding of deep learning techniques, model fine-tuning, hyper parameter optimization, and model optimization
- Proven experience in deploying and managing ML models in production environments.
- Excellent analytical skills, with a problem-solving mindset and the ability to think strategically.
- Strong communication skills for articulating complex concepts to diverse audiences.
- Working knowledge or experience in MLOps and LLMOps using tools like mlflow, kubeflow
- Working knowledge of basic software engineering principles and best practices
- Demonstrated working knowledge and experience on classic ML techniques and frameworks.
- Nice to have : Knowledge of Cloud vendor based ML Platforms such as Azure ML, Sagemaker
Who you are as a person/leader
- Owner mentality - For you, the buck stops at you, You own it, you will learn it, and you will get it done
- You are naturally curious. Always experimenting than hypothesizing - You like to push boundaries, you figure things out and experiment your way through any problem
- You are passionate, unafraid & loyal to the team & mission
- You love to learn & win together
- You communicate well through voice, writing, chat or video, and work well with a remote/global team
Nice to have competencies
- Large/Complex organization experience in deploying NLP/ML in production
- Experience in efficiently scaling ML model training and inferencing
- Experience with Big Data technologies using Kafka, Spark, Hadoop, Snowflake
What We Offer
- A chance to make a real impact in the future of healthcare
- Autonomy, ownership, and the ability to chart your own growth path
- Competitive compensation and benefits
- 100% employer-paid health, vision, and dental insurance
- Retirement plans (401k), disability insurance, employee assistance programs
Send your resume and a brief cover letter to [email protected] explaining why you're the right partner for this mission.
Skills Required
- Bachelor's or Master's degree in Computer Science, Engineering, Data Science, or a related field
- 5-7 years of experience in machine learning engineering
- Significant experience developing production-grade models and model pipelines in a regulated industry such as healthcare
- Hands-on expertise with large language models, computer vision models, and classic NLP technologies
- Proficiency in Python
- Extensive experience with TensorFlow, PyTorch, OpenCV, or similar machine learning libraries and frameworks
- Strong understanding of deep learning, model fine-tuning, hyperparameter optimization, and model optimization
- Experience deploying and managing machine learning models in production environments
- Working knowledge or experience with MLOps and LLMOps tools such as MLflow and Kubeflow
- Working knowledge of software engineering principles and best practices
- Working knowledge and experience with classic machine learning techniques and frameworks
- Experience with distributed training on GPUs and TPUs
- Knowledge of cloud machine learning platforms such as Azure ML or SageMaker
- Experience deploying NLP or machine learning systems in large or complex organizations
- Experience scaling machine learning model training and inference efficiently
- Experience with Kafka, Spark, Hadoop, or Snowflake
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The Company
What We Do
Autonomize AI Agents & Copilots organize, contextualize and summarize unstructured data to reduce the administrative burden for healthcare knowledge workers to make data-driven decisions and improve patient outcomes. Our customers include health plans, providers and life sciences companies. Unlike generic AI systems retrofitted for healthcare, Autonomize deeply understands medical contexts, terminologies, and operational nuances. Our healthcare-focused AI Agents & Copilots augment knowledge work, drastically reducing administrative burden. Care management teams spend 78% less time per case, achieving an impressive 85% boost in case review efficiency. Prior authorization processes that traditionally take 20-30 minutes shrink to mere seconds, accompanied by an 80% reduction in manual errors, saving millions of dollars annually. Our AI Agents turn chaotic, unstructured healthcare data—clinical notes, PDFs, faxes, and claims—into structured, contextual information that informs decisions and actions. This has driven substantial real-world impact: organizations using Autonomize experience a 92% reduction in manual effort for care gaps and HEDIS chart reviews, dramatically improving compliance and STAR ratings. Autonomize AI is purpose-built for healthcare, transforming healthcare operations one workflow at a time through AI-native solutions that deliver immediate, scalable impact.
