Machine Learning Engineer

C the Signs·Remote(United States)
AI & Machine Learning
Excel

WFA Digital Insight

As the demand for AI and machine learning specialists continues to soar, with a 25% increase in job openings in the past year, skilled professionals are in high demand. C the Signs is at the forefront of this trend, leveraging AI to transform the healthcare sector. With the ability to work remotely, this role offers the perfect blend of innovation and flexibility. Candidates should be prepared to showcase their expertise in machine learning engineering, data preprocessing, and model training, as well as their ability to collaborate with cross-functional teams. Before applying, it's essential to understand the company's commitment to using AI for social good and its impact on patient outcomes.

Job Description

About the Role

The Machine Learning Engineer will play a crucial role in the development and deployment of large language and machine learning models, focusing on data preprocessing, model training, and fine-tuning using extensive healthcare datasets. This role requires a strong understanding of large language models, machine learning principles, and data engineering, as well as experience working with sensitive healthcare data. As part of the team, you will collaborate with data scientists, clinicians, and software engineers to integrate models into production systems, ensuring data privacy and security compliance.

The day-to-day responsibilities will involve designing, training, and fine-tuning various large language models to solve specific clinical or operational problems. You will set up and manage the training environment, including GPU instances and required software, and experiment with hyperparameters to optimize model performance. Your work will have a direct impact on patient outcomes, making this role both challenging and rewarding.

What You Will Do

  • Clean, transform, and prepare large, complex healthcare datasets for machine learning model development
  • Handle missing values, outlier detection, feature engineering, and data normalization
  • Identify, collect, and curate relevant, industry-specific datasets for model retraining
  • Format data appropriately for the chosen large language model and training pipeline
  • Design, train, and fine-tune various large language models on extensive healthcare data
  • Set up and manage the training environment, including GPU instances and required software
  • Experiment with and fine-tune hyperparameters to optimize model performance
  • Integrate structured and unstructured data into multi-modal/multi-input models
  • Evaluate model performance using appropriate metrics and implement optimization strategies
  • Develop and maintain robust and scalable data and ML pipelines for model training, inference, and deployment
  • Collaborate with data scientists, clinicians, and software engineers to understand requirements and integrate models into production systems

What We Are Looking For

  • Bachelor's or Master's degree in Computer Science, Machine Learning, Artificial Intelligence, or a related quantitative field
  • 5+ years of experience in Machine Learning Engineering or a similar role
  • Proven experience with large-scale data preprocessing, large language model training, and fine-tuning
  • Experience with distributed training and GPU/TPU optimization
  • Strong understanding of various machine learning algorithms and deep learning architectures
  • Proficiency in Python and relevant ML libraries
  • Experience working with healthcare data is highly desirable
  • Excellent problem-solving and analytical skills
  • Strong communication and collaboration abilities

Nice to Have

  • Experience with cloud platforms and distributed computing frameworks
  • Familiarity with MLOps practices and tools
  • Certification in machine learning or a related field

Benefits and Perks

  • Competitive salary and benefits package
  • Flexible working arrangements, including remote or hybrid options
  • The opportunity to work on life-changing AI technology that directly impacts patient outcomes
  • Collaborative and dynamic work environment
  • Professional development opportunities
  • Access to the latest tools and technologies

How to Stand Out

  • To stand out, showcase your experience with large language models and machine learning engineering, highlighting specific projects and results.
  • Be prepared to discuss your approach to data preprocessing, model training, and fine-tuning, as well as your understanding of healthcare data privacy and security.
  • Familiarize yourself with the company's commitment to using AI for social good and be ready to explain how your skills align with this mission.
  • When negotiating salary, consider the value of the company's flexible working arrangements and opportunities for professional growth.
  • Pay attention to the company culture and values during the interview process, ensuring they align with your own goals and expectations.
  • Prepare to back up your claims with examples from your portfolio or previous experience, demonstrating your ability to collaborate with cross-functional teams.

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