• Build production augmentation and profile models: Design, train, evaluate, and deploy models against real survey data, owning the full path from experiment to production. • Innovate on solutions: We aim to build the best simulation of survey responses in the industry. You'll design the experiments, baselines, and models to get us there. • Use the latest technology: Build on the best open-weights LLMs with fine-tuning, build new ones from scratch with custom architectures or use third party models through an API; whatever gets us the best result. • Partner with Data Science: Work with the data science team to ensure integrity of our statistical testing and experiment framework. • Leverage AI tooling: Use Claude Code and agentic programming tools where appropriate.

Role Description Meta is seeking talented engineers to join our teams in building cutting-edge products that connect billions of people around the world. As a member of our team, you will have the opportunity to work on complex technical problems, build new features, and improve existing products across various platforms, including mobile devices and web applications. Our teams are constantly pushing the boundaries of user experience, and we're looking for passionate individuals who can help us advance the way people connect globally. If you're interested in joining a world-class team of industry veterans and working on exciting projects that have a significant impact, we encourage you to apply. - Collaborate with cross-functional teams (product, design, operations, infrastructure) to build innovative application experiences - Implement custom user interfaces using latest programming techniques and technologies - Develop reusable software components for interfacing with back-end platforms - Analyze and optimize code for quality, efficiency, and performance - Lead complex technical or product efforts and provide technical guidance to peers - Architect efficient and scalable systems that drive complex applications - Identify and resolve performance and scalability issues - Work on a variety of coding languages and technologies - Establish ownership of components, features, or systems with expert end-to-end understanding Qualifications - Bachelor's degree in Computer Science, Computer Engineering, relevant technical field, or equivalent practical experience - Track record of setting technical direction for a team, driving consensus and successful cross-functional partnerships - 6+ years of programming experience in a relevant language or 3+ years of experience + PhD - Experience building maintainable and testable code bases, including API design and unit testing techniques Requirements - Experience building and shipping high quality work and achieving high reliability - Experience improving quality through thoughtful code reviews, appropriate testing, proper rollout, monitoring, and proactive changes - Experience with developing machine learning models at scale from inception to business impact - Exposure to architectural patterns of large scale software applications - Experience with scripting languages such as PyTorch, TensorFlow, Python, JavaScript or Hack - 2+ years of experience in one or more of the following areas: machine learning, recommendation systems, pattern recognition, data mining, artificial intelligence, or a related technical field - Knowledge developing and debugging in C/C++ and Java - Demonstrated ability to integrate AI tools to optimize/redesign workflows and drive measurable impact (e.g., efficiency gains, quality improvements) - Experience adhering to and implementing responsible, ethical AI practices (e.g., risk assessment, bias mitigation, quality and accuracy reviews) - Demonstrated ongoing AI skill development (e.g., prompt/context engineering, agent orchestration) and staying current with emerging AI technologies Benefits - $154,003/year to $217,000/year + bonus + equity + benefits

Role Description Are you excited by the idea of building real, production AI systems that directly impact patient care? Trissential is partnering with a leading healthcare organization to hire AI/ML Engineers who thrive at the intersection of advanced AI, cloud engineering, and healthcare delivery. This is not an experimental or academic role. You’ll design, build, evaluate, and deploy production-grade AI and agentic systems that clinicians and operational teams rely on every day. Your Role & Responsibilities - Design and implement agentic AI systems, including LLM integrations, orchestration patterns, prompt engineering, and tool-using agents. - Build and maintain production Python services, APIs, automation workflows, and data pipelines. - Develop RAG pipelines using embeddings, vector databases, and structured/unstructured healthcare data. - Implement evaluation frameworks and guardrails to address hallucinations, safety, PHI protection, and quality before production. - Deploy, monitor, and optimize AI/ML solutions in cloud environments using CI/CD pipelines. - Collaborate cross-functionally to translate clinical and operational needs into scalable AI solutions. - Ensure solutions meet healthcare regulatory, quality, and risk mitigation standards. - Contribute to AI/ML best practices, MLOps/LLMOps methodologies, and continuous improvement. Qualifications - 7+ years of experience in software engineering, ML engineering, or AI engineering. - Strong Python experience in production environments (APIs, async workflows, testing). - Hands-on experience designing and deploying AI / LLM agent systems. - Experience with at least one agent framework such as LangChain, LangGraph, LlamaIndex, Semantic Kernel, or Google ADK. - Practical experience with RAG architectures, embeddings, and vector databases. - Experience deploying AI systems in Azure, AWS, or GCP. - Understanding of HIPAA / PHI handling, including de-identification and secure AI workflows. - Strong communication skills and ability to explain complex AI concepts to non-technical stakeholders. Bonus Points If You Have - Experience with Google Cloud or Azure in regulated environments. - MCP, A2A, or protocol-driven AI architectures. - Experience with BigQuery, Firestore, Cloud SQL, or Dataflow. - MLOps or LLMOps experience in enterprise environments. - Infrastructure as Code (Terraform) and CI/CD (Azure DevOps Pipelines). - Experience with healthcare informatics standards and clinical data models. Education & Certifications You Need - Master’s degree in Engineering, Computer Science, Mathematics, Health Science, or related field with 1+ year experience OR - Bachelor’s degree with 3+ years of relevant experience OR - HS Diploma/GED with 7+ years of experience. Benefits - Competitive Compensation – $85–$95 per hour. Final compensation is determined based on skill alignment, years of experience, and fair, market-based rates by geography. - Comprehensive Benefits for you and your dependents – Medical, dental, vision, free tele-health, HSA with company contribution, life and disability insurance, and 401k with matching. - Paid Time Off – Offers paid time away from work. - High-Impact Healthcare Work – Build AI systems used in real clinical environments. - Professional Growth – Work alongside senior AI engineers, clinicians, and healthcare leaders.

• Develop and maintain model training, validation and inference pipelines. • Design feature and data pipelines that support machine learning applications in production. • Convert models developed in exploratory environments into reproducible, scalable pipelines. • Integrate machine learning solutions into the organization’s data ecosystem. • Collaborate with engineering, DevOps and DataOps teams to evolve the platform and the organization’s machine learning practices. • Work closely with data scientists to operationalize analytical models. • Support the transition of models from exploratory environments to production pipelines. • Build pipelines that enable recurring training, updating and model evolution. • Ensure models can run at scale and meet performance requirements for operational use. • Prepare, structure and transform the data required for model training and execution. • Develop data pipelines and feature engineering focused on machine learning applications. • Work with data engineering to ensure the availability and quality of the data used by models. • Implement monitoring mechanisms for model performance, stability and behavior in production. • Support the identification of model degradation and the need for retraining. • Contribute to the continuous improvement of machine learning solutions in production. • Ensure software development best practices are applied to machine learning solutions. • Use code versioning, code review and engineering practices for developing ML pipelines. • Contribute to the standardization and evolution of the area’s machine learning engineering practices.