Machine learning engineer leading the design and build of an agentic AI system to automatically resolve insurance claim denials for healthcare practices.
Compensation
The base pay range for this role is $150,000–$275,000 per year.
Responsibilities
Design and build the architecture for our agentic AI system that autonomously resolves insurance claim denials
Develop specialized AI agents for denial classification, root cause analysis, evidence retrieval, policy reasoning, and appeal generation
Implement multi-agent orchestration frameworks that coordinate complex workflows across research, decision-making, and document generation
Build and optimize RAG systems to retrieve relevant clinical documentation, billing records, and payer policy information
Create evaluation frameworks and feedback loops to continuously improve agent performance and reliability
Requirements
Nice to have: healthcare data experience
Nice to have: understanding of medical billing workflows
We are seeking a Data Scientist / Senior Data Scientist with 5–7 years of experience in data science, machine learning, advanced analytics, and AI solution development, with a strong emphasis on Generative AI (GenAI) and AI application development.
Compensation
We offer competitive compensation and comprehensive benefits and programs.
Responsibilities
Analyze large, complex datasets to uncover trends, patterns, anomalies, and actionable business insights.
Design, develop, and deploy AI and GenAI applications that address business needs using machine learning, large language models, and intelligent workflow orchestration.
Build and optimize predictive, statistical, and time series models for business and operational use cases.
Develop robust data pipelines, ETL workflows, and feature engineering processes using SQL and Python to support AI/ML applications.
Lead Python-based application development for automation, data transformation, model integration, API development, and AI-powered workflows.
Requirements
Bachelor's degree in Computer Science, Data Science, Engineering, AI, or a related field
Nice to have: Dataiku
Nice to have: Tableau
Nice to have: domain awareness of industrial systems such as compressors, motors, power systems, refrigeration systems, and IC engines
Senior MLOps & Cloud Infrastructure Engineer responsible for building, operating, and evolving the cloud infrastructure and engineering platforms behind production AI systems.
Responsibilities
Design, provision, and operate scalable infrastructure on GCP
Manage containerised applications and ML workloads with Kubernetes
Build reproducible infrastructure using Terraform
Design and improve CI/CD pipelines for backend services and data pipelines
Improve service availability, resilience, and incident response
Washington, D.C., District of Columbia, United States
United States
Responsibilities
Design, develop, and deploy production‑grade AI‑powered solutions spanning classical ML, Generative AI (LLMs, RAG, Graph RAG), and multi‑agent orchestration frameworks
Own the end‑to‑end AI/ML technical roadmap, defining architectural direction, choosing optimal technologies, and establishing engineering and coding standards
Use enterprise‑approved AI tools to streamline workflows, automate tasks, and drive continuous improvement across the development lifecycle
Evaluate emerging trends and technologies to inform high‑quality solution design and strategic innovation
Senior ML Engineer to build customer-facing AI platform and deliver production ML systems
Compensation
For this position, our hiring range for base annual pay is estimated to be CAD $160-200k at the time of this posting
Responsibilities
Take customer problems from idea to working prototype quickly, prove or kill an approach with data, and move what works into production
Train, fine-tune, and evaluate models for matching, classification, and extraction problems
Design and ship RAG systems, embedding-based retrieval, and LLM-powered features on AWS
Own the full ML lifecycle: experiment tracking, model versioning, deployment, monitoring, and retraining
Build evaluation that tells us whether something works: labeled golden sets, offline metrics (precision, recall), LLM-as-judge where it fits, and shadow or A/B testing before rollout
Lead design and delivery of Data Science and AI solutions across global operations, mentor junior talent, and translate insights into measurable business outcomes.
Responsibilities
Partner with business leaders to identify high-value opportunities
Collaborate with domain experts, business analysts, data engineers, software engineers, and integration teams
Lead cross-business-unit and global data science initiatives
Develop and validate machine learning models
Deploy and maintain production-ready models and AI applications
Requirements
Degree in a quantitative discipline such as Computer Science, Engineering, Statistics, or Mathematics
Nice to have: Knowledge of sales, manufacturing operations, supply chain, ERP systems, or finance
Build a comprehensive managed platform for the AI Model Lifecycle, focusing on fine‑tuning, training, and deployment of large language models.
Compensation
Compensation will be paid in the range of up to $250,000 - $300,000 + Bonus. Restricted Stock Units are included in all offers.
Responsibilities
Manage fine‑tuning systems for large foundation models (SFT, PEFT, LoRA, adapters), including multi‑node orchestration, checkpointing, failure recovery, and cost‑efficient scaling.
Implement and maintain end‑to‑end training pipelines for Large Language Models.
Apply RFT and reinforcement learning to fine‑tuning and training.
This role is focused on ML engineering and MLOps execution, including pipeline development, model evaluation automation, and data / model lineage. It is not primarily a research or advanced modeling role.
Responsibilities
Build and maintain reproducible ML pipelines for Computer Vision and GenAI use cases
Convert notebook-based or ad hoc workflows into version-controlled, testable, and maintainable pipeline components
Standardize training, evaluation, and monitoring workflows across projects
Improve data validation, artifact management, and handoff between data preparation, model inference, and downstream delivery
Build and maintain CI/CD workflows for ML code, including automated testing and containerization
Drive MLOps maturity across development pipelines, overseeing model versioning, CI/CD for ML, retraining automation, drift detection, and production monitoring
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