Staff Machine Learning Engineer

On behalf of our client, Dice Talent Solutions is seeking a Staff Machine Learning Engineer to help establish and lead our Machine Learning Engineering function. This role will be instrumental in shaping the future of our client s ML and LLM capabilities, driving innovation across traditional ML pipelines, AI/LLM applications, and ensuring fairness, scalability, and cost-efficiency in our systems.

You'll work directly with the Director of Data, AI, and ML and partner closely with engineering and product leadership to grow and mentor a team of Machine Learning Engineers. Your impact will shape both the technical direction of our ML systems and the culture of our growing team.

This is a direct hire (full-time) salaried position with our healthcare industry client. Work is performed remotely from the continental United States. Our client is unable to provide visa sponsorship or transfers now or in the future. No 3rd party Corp to Corp inquiries will be considered.

Responsibilities

• Architect and lead the development of scalable ML infrastructure for training, inference, and model lifecycle management.

• Design and implement systems for ML experiment tracking, model registry, and feature store integration.

• Establish frameworks for bias and fairness assessment across ML models and ensure compliance with internal and external standards.

• Develop infrastructure and tooling for real-time and batch model inference.

• Collaborate with data scientists, engineers, and product teams to translate business needs into new ML solutions.

• Support Software Engineering teams in their development of LLM products by establishing and managing shared LLM infrastructure for governance, observability, and cost management.

• Mentor and guide a team of ML engineers, fostering a culture of innovation, transparency, and technical excellence.

Required:

• 7+ years of experience in Machine Learning Engineering or Applied ML, with 2+ years of leadership experience.

• Expertise in ML infrastructure, model lifecycle management, and experiment tracking tools (e.g., MLflow, Weights & Biases).

• Hands-on experience with LLM development, including prompt tuning, RAG architectures, and vector databases (e.g., Qdrant, Pinecone).

• Strong understanding of bias and fairness metrics, and experience implementing auditing frameworks.

• Proficiency in big data tools and cloud platforms (e.g., AWS, Google Cloud Platform, Azure, Snowflake, Spark).

• Strong software engineering skills in Python and experience with containerization and orchestration (e.g., Docker, Kubernetes).

• Experience managing ML costs and optimizing cloud resource usage.

• Excellent communication and collaboration skills, with a track record of cross-functional leadership.

• Passion for building scalable, transparent, and impactful ML systems.

Preferred:

• Experience in a startup environment

• Bachelor's Degree in Computer Science or similar degree

• Experience with boosted models like XGBoost

• Experience with LLM Voice agents

• Familiarity with Healthcare data