Sr. Software Engineer, Machine Learning

TBC Corporation

22h•$122,425 - $183,638

About The Position

Lyric is an AI-first, platform-based healthcare technology company, committed to simplifying the business of care by preventing inaccurate payments and reducing overall waste in the healthcare ecosystem, enabling more efficient use of resources to reduce the cost of care for payers, providers, and patients. Lyric, formerly ClaimsXten, is a market leader with 35 years of pre-pay editing expertise, dedicated teams, and top technology. Lyric is proud to be recognized as 2025 Best in KLAS for Pre-Payment Accuracy and Integrity and is HI-TRUST and SOC2 certified, and a recipient of the 2025 CandE Award for Candidate Experience. Interested in shaping the future of healthcare with AI? Explore opportunities at lyric.ai/careers and drive innovation with #YouToThePowerOfAI. Applicants must already be legally authorized to work in the U.S. Visa sponsorship/sponsorship assumption and other immigration support are not available for this position. We are looking for a highly skilled Machine Learning Engineer with hands-on experience in designing, building, and deploying ML models at scale. You will work on end-to-end ML pipelines—from data preprocessing to production deployment—leveraging modern frameworks and MLOps practices. This role is ideal for someone who thrives in solving complex problems, optimizing workflows, and applying AI to deliver impactful business solutions. Additionally, you will collaborate with analytics teams to design dashboards and visualizations that provide actionable insights for stakeholders. ESSENTIAL JOB RESPONSIBILITIES & KEY PERFORMANCE OUTCOMES Model Development & Deployment Design, train, and optimize ML models using PyTorch or TensorFlow for production-grade applications. Build scalable data pipelines for feature engineering and model training using Pandas, Dask, or equivalent frameworks. Implement model evaluation, hyperparameter tuning, and performance monitoring. MLOps Develop and maintain ML workflows using Airflow, Kedro, and MLflow for reproducibility and traceability. Automate model deployment and lifecycle management across environments (dev, staging, production). Data Engineering & Processing Handle large-scale datasets efficiently using distributed computing frameworks (Dask, Spark). Ensure data quality, consistency, and compliance with governance standards. Exposure to Snowflake or Databricks is a plus. Monitoring & Observability Implement model drift detection, performance tracking, and automated retraining strategies. Use experiment tracking tools (MLflow, Weights & Biases) for transparency and reproducibility. Collaboration & Documentation Work closely with data scientists, software engineers, and product teams to align ML solutions with business goals. Document ML workflows, best practices, and operational guidelines. Model Development & Deployment Design, train, and optimize ML models using PyTorch or TensorFlow for production-grade applications. Build scalable data pipelines for feature engineering and model training using Pandas, Dask, or equivalent frameworks. Implement model evaluation, hyperparameter tuning, and performance monitoring. MLOps Develop and maintain ML workflows using Airflow, Kedro, and MLflow for reproducibility and traceability. Automate model deployment and lifecycle management across environments (dev, staging, production). Data Engineering & Processing Handle large-scale datasets efficiently using distributed computing frameworks (Dask, Spark). Ensure data quality, consistency, and compliance with governance standards. Exposure to Snowflake or Databricks is a plus. Monitoring & Observability Implement model drift detection, performance tracking, and automated retraining strategies. Use experiment tracking tools (MLflow, Weights & Biases) for transparency and reproducibility. Collaboration & Documentation Work closely with data scientists, software engineers, and product teams to align ML solutions with business goals. Document ML workflows, best practices, and operational guidelines.

Requirements

5–7 years of experience in ML engineering or applied machine learning.
Strong proficiency in Python and libraries like Pandas, Dask, NumPy, Scikit-learn.
Hands-on experience with PyTorch or TensorFlow for model development.
Solid understanding of MLOps tools: Airflow, Kedro, MLflow (or equivalents).
Experience deploying ML models in production environments (APIs, batch jobs, streaming).
Strong problem-solving skills and ability to work in agile, fast-paced environments.

Nice To Haves

Experience with feature stores (Feast, Tecton) and data versioning tools (DVC).
Knowledge of distributed training and GPU optimization.
Experience with Power BI or similar BI tools for analytics and visualization.
Understanding of model explainability and responsible AI practices.
Familiarity with containerization (Docker) and orchestration (Kubernetes).
Exposure to cloud platforms (Azure, AWS, or GCP) for ML workloads.
Contributions to open-source ML projects or technical blogs.

Responsibilities

Design, train, and optimize ML models using PyTorch or TensorFlow for production-grade applications.
Build scalable data pipelines for feature engineering and model training using Pandas, Dask, or equivalent frameworks.
Implement model evaluation, hyperparameter tuning, and performance monitoring.
Develop and maintain ML workflows using Airflow, Kedro, and MLflow for reproducibility and traceability.
Automate model deployment and lifecycle management across environments (dev, staging, production).
Handle large-scale datasets efficiently using distributed computing frameworks (Dask, Spark).
Ensure data quality, consistency, and compliance with governance standards.
Implement model drift detection, performance tracking, and automated retraining strategies.
Use experiment tracking tools (MLflow, Weights & Biases) for transparency and reproducibility.
Work closely with data scientists, software engineers, and product teams to align ML solutions with business goals.
Document ML workflows, best practices, and operational guidelines.
Design, train, and optimize ML models using PyTorch or TensorFlow for production-grade applications.
Build scalable data pipelines for feature engineering and model training using Pandas, Dask, or equivalent frameworks.
Implement model evaluation, hyperparameter tuning, and performance monitoring.
Develop and maintain ML workflows using Airflow, Kedro, and MLflow for reproducibility and traceability.
Automate model deployment and lifecycle management across environments (dev, staging, production).
Handle large-scale datasets efficiently using distributed computing frameworks (Dask, Spark).
Ensure data quality, consistency, and compliance with governance standards.
Implement model drift detection, performance tracking, and automated retraining strategies.
Use experiment tracking tools (MLflow, Weights & Biases) for transparency and reproducibility.
Work closely with data scientists, software engineers, and product teams to align ML solutions with business goals.
Document ML workflows, best practices, and operational guidelines.