Senior Machine Learning Engineer

{
  "content_hash": "78f7727d776af3ab3daf626abd9295e22e82349d2cec908c28bb3e282c787bad",
  "source_hash": "15a5ae45751facd185dae100fa6a4872ad5accb3a31b292b5f0316e12c1b0e07",
  "last_changed_at": "2026-05-31T17:47:30.512Z",
  "active_status": "active"
}

{
  "language": "en",
  "location": {
    "raw": "Boston, United States (Remote)",
    "city": "Boston",
    "region": null,
    "country": "United States",
    "is_remote": true,
    "confidence": 0.95
  },
  "salary_max": null,
  "salary_min": null,
  "inferred_at": "2026-06-06T13:32:14.641Z",
  "launch_scope": {
    "reason": "english_us_canada",
    "included": true,
    "language": "en",
    "location": {
      "raw": "Boston, United States (Remote)",
      "city": "Boston",
      "region": null,
      "country": "United States",
      "is_remote": true,
      "confidence": 0.95
    },
    "countries": [
      "United States"
    ]
  },
  "remote_policy": "remote",
  "salary_period": null,
  "workplace_type": "remote",
  "salary_currency": null
}

{}

{
  "detail": {
    "type": "Full-time",
    "title": "Senior Machine Learning Engineer",
    "posted": "2026-04-28",
    "company": "C the Signs",
    "applyUrl": "https://apply.workable.com/c-the-signs/j/B9D97E165E/apply",
    "location": "Boston, United States (Remote)",
    "workplace": "remote",
    "department": null,
    "descriptionText": "Description\n\n Position Summary\n\nThe Machine Learning Engineer will be responsible for the end to end development and deployment of Large language and machine learning models, with a primary focus on data preprocessing, model training, and fine tuning using large scale healthcare datasets. This role requires a strong understanding of Large language models, machine learning principles, data engineering, and experience working with sensitive healthcare data.\n\n Key Responsibilities\n\n Data Preprocessing: Clean, transform, and prepare large, complex healthcare datasets for machine learning model development. This includes handling 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 LLM and training pipeline\n Model Training & Fine Tuning: Design, train, and fine tune various LLMs on extensive healthcare data to solve specific clinical or operational problems. Set up and manage the training environment, including GPU instances and required software. Train and fine tune pre trained LLMs on the custom dataset to achieve specific goals. Experiment with and fine tune hyperparameters such as learning rate, batch size, and training epochs to optimize model performance. Integration of structured + unstructured data (multi modal/multi input models)\n Model Evaluation & Optimization: Evaluate model performance using appropriate metrics, identify areas for improvement, and implement optimization strategies.\n Pipeline Development: Develop and maintain robust and scalable data and ML pipelines for model training, inference, and deployment.\n Collaboration: Work closely with data scientists, clinicians, and software engineers to understand requirements, integrate models into production systems, and ensure data privacy and security compliance.\n Research & Development: Stay up to date with the latest advancements in machine learning and healthcare AI, and explore new technologies and methodologies to enhance our solutions.\n Documentation: Maintain clear and comprehensive documentation of models, data pipelines, and experimental results.\n\n Requirements\n\n Education: Bachelor's or Master's degree in Computer Science, Machine Learning, Artificial Intelligence, or a related quantitative field.\n Experience:\n\n 5+ years of experience in Machine Learning Engineering or a similar role.\n Proven experience with large scale data preprocessing, LLM/model training, and fine tuning.\n Experience with distributed training (PyTorch Distributed, DeepSpeed, Ray, Hugging Face Accelerate).\n Experience with GPU/TPU optimization, memory management for large language models.\n Experience working with healthcare data is highly desirable.\n\n Technical Skills:\n\n Proficiency in Python and relevant ML libraries (e.g., TensorFlow, PyTorch, Scikit learn, Pandas, NumPy).\n Strong understanding of various machine learning algorithms,Large Language Models, and deep learning architectures.\n Experience with cloud platforms (e.g., GCP, AWS) and distributed computing frameworks (e.g., Spark) is a plus.\n Familiarity with MLOps practices and tools.\n\n Soft Skills:\n\n Excellent problem solving and analytical skills.\n Strong communication and collaboration abilities.\n Ability to work independently and as part of a team in a fast paced environment.\n\n Work Authorization:\n\n Must be a US Citizen, Green Card holder, or currently in the US have valid H1B visa\n\n Benefits\n\n Why Join Us? \n\nJoining  C the Signs  is not just about building AI; it’s about shaping the future of healthcare. If you are a technical leader with an unshakable belief in the power of AI to save lives and the ability to make it happen at scale, this is your opportunity to create a tangible, global impact.\n\n Benefits: \n\n Competitive salary and benefits package.\n Flexible working arrangements (remote or hybrid options available).\n The opportunity to work on life changing AI technology that directly impacts patient outcomes.\n Join a team that combines cutting edge innovation with a mission to save lives and improve health equity.\n Continuous learning opportunities with access to the latest tools and advancements in AI and healthcare."
  },
  "list_job": {
    "id": "B9D97E165E",
    "type": "Full-time",
    "title": "Senior Machine Learning Engineer",
    "posted": "2026-04-28",
    "salary": null,
    "location": "Boston, United States (Remote)",
    "detailUrl": "https://apply.workable.com/c-the-signs/jobs/view/B9D97E165E.md",
    "department": "Other"
  },
  "detail_meta": {
    "url": "https://apply.workable.com/c-the-signs/jobs/view/B9D97E165E.md",
    "http_status": 200,
    "content_type": "text/markdown; charset=utf-8",
    "response_bytes": 4656
  },
  "detail_errors": []
}