Autonomize
About Autonomize AI
Autonomize AI is on a mission to help organizations make sense of the world's data. We help organizations harness the full potential of data to unlock business outcomes. Unstructured dark data contains nuggets of information that when paired with human context will unlock some of the most impactful insights for most organizations, and it’s our goal to make that process effortless and accessible.
We are an ambitious team committed to human-machine collaboration. Our founders are serial entrepreneurs passionate about data and AI and have started and scaled several companies to successful exits. We are a global, remote company with expertise in building amazing data products, captivating human experiences, disrupting industries, being ridiculously funny, and of course scaling AI
The Opportunity
As a Senior Machine Learning Engineer at Autonomize, you will lead the development and deployment of advanced machine learning systems with a strong emphasis on Large Language Models (LLMs), Vision-Language Models (VLMs), and classical NLP systems. You will play a critical role in advancing our AI-powered healthcare copilots and autonomous agents, helping healthcare organizations unlock efficiency, accuracy, and intelligence across complex workflows.d Agents.
What You’ll Do
  1. Fine-tune, adapt, and prompt-engineer Large Language Models (LLMs) for diverse healthcare applications across customer engagements.
  2. Design and refine approaches for processing vision-based healthcare data using state-of-the-art Vision-Language Models (VLMs) to accurately interpret medical documents, healthcare forms, and multimodal inputs.
  3. Develop and enhance classical NLP systems to support clinical documentation, summarization, patient interaction workflows, and structured data extraction.
  4. Work across encoder-only, encoder-decoder, and decoder-only architectures — with the ability to evaluate, select, and justify the right model architecture based on task constraints, latency, cost, and performance requirements.
  5. Build and operationalize supervised fine-tuning (SFT) pipelines that go beyond framework-level engineering, incorporating data curation, quality evaluation, instruction tuning strategies, and performance optimization.
  6. Contribute to dataset strategy, including designing, generating, validating, and scaling high-quality datasets — including synthetic and augmented data pipelines where appropriate.
  7. Collaborate closely with data scientists, ML engineers, healthcare clients, and product managers to deliver production-grade AI solutions.
  8. Ensure models are efficiently deployed into healthcare systems with high performance, scalability, and reliability.
  9. Lead rigorous evaluation, testing, validation, and tuning processes to meet healthcare-grade accuracy, safety, and compliance standards.
  10. Drive distributed training workflows across GPUs/TPUs and optimize training/inference efficiency.
  11. Mentor junior engineers and data scientists, fostering technical excellence and a culture of experimentation and ownership.
  12. Stay current with emerging research and tooling across LLMs, VLMs, NLP, and healthcare AI.
  13. Clearly document methodologies, architectural decisions, and outcomes for both technical and non-technical stakeholders.
You’re a Fit If You Have
1. Bachelor’s or Master’s degree in Computer Science, Engineering, Data Science, or a related field.
2. 5–7 years of experience in machine learning engineering, including building and deploying production-grade models and pipelines in regulated industries such as healthcare.
3. Strong hands-on expertise with:
  • Large Language Models (e.g., GPT, BERT, open-weight LLMs)
  • Vision and Vision-Language Models
  • Classical NLP and ML systems
4. Deep understanding of:
  • Encoder-only, encoder-decoder, and decoder-only architectures
  • Model selection tradeoffs across tasks
  • Fine-tuning strategies (SFT, instruction tuning, parameter-efficient methods)
  • Hyperparameter optimization and model compression techniques
5. Experience designing and implementing robust SFT pipelines, including dataset curation, evaluation loops, and quality control beyond basic framework orchestration.
6. Strong intuition and practical experience in building datasets — including synthetic data generation and augmentation strategies to improve model robustness and generalization.
7. Proficiency in Python and ML frameworks such as PyTorch, TensorFlow, OpenCV, etc.
8. Proven experience deploying and managing ML models in production environments.
9. Working knowledge of MLOps and LLMOps tools such as MLflow and Kubeflow.
10. Solid understanding of software engineering best practices and system design principles.
11. Excellent analytical thinking and problem-solving ability.
12. Strong communication skills, capable of articulating complex AI concepts to cross-functional and non-technical stakeholders
13. Nice to Have:
  • Experience with prompt optimization frameworks such as DSPy.
  • Familiarity with cloud-based ML platforms such as Azure ML or SageMaker.
What we offer:
  • Influence & Impact: Lead and shape the future of healthcare AI implementations
  • Outsized Opportunity: Join at the ground floor of a rapidly scaling, VC-backed startup
  • Ownership, Autonomy & Mastery: Full-stack ownership of customer programs, with freedom to innovate
  • Learning & Growth: Constant exposure to founders, customers, and new technologies—plus a professional development budget
We are an equal opportunity employer and do not discriminate on the basis of race, religion, national origin, gender, sexual orientation, age, veteran status, disability, or other legally protected statuses.
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