• Design, develop, and optimize computer vision models for real-time applications on embedded systems • Implement model compression techniques such as quantization, pruning, and knowledge distillation to improve performance on low-power hardware • Deploy machine learning models on embedded platforms, including ARM, NVIDIA Jetson, Qualcomm, or custom ASICs • Write clean, efficient, and well-documented code in Python and C++, leveraging ML frameworks like TensorFlow, PyTorch, and ONNX • Develop and fine-tune SLAM, object detection, tracking, and feature extraction models for high efficiency • Collaborate with cross-functional teams to integrate ML models into production systems, optimizing for latency, accuracy, and power consumption • Benchmark and profile ML models to identify and implement optimizations for inference on embedded hardware • Research and apply cutting-edge ML techniques to improve real-time performance in resource-constrained environments

• Package and deploy models (ASR, translation, OCR, NER, summarization) using Triton/KServe on Kubernetes. • Build evaluation pipelines (WER, BLEU, F1, latency, cost) and automate release gating. • Operate streaming + batch inference via Kafka, Temporal, and backfill tooling. • Monitor drift/quality with Prometheus, Grafana, Evidently; optimize inference cost and performance. • Collaborate with TypeScript teams on payload schemas, contracts, and human-in-the-loop feedback loops.

ABOUT US AND ABOUT YOU Clinician Nexus enables health care organizations to build thriving clinician teams with industry-leading technology products, workforce and compensation analytics, and automated workflow solutions. Backed by extensive technical expertise and industry-leading data, we deliver innovative approaches to help clients plan, educate, and engage their clinical workforce at every stage of the lifecycle. We are committed to providing our clients with outstanding guidance and support as they work to shape the future of health care. JOB SUMMARY We are seeking a highly skilled and motivated Machine Learning Engineer to join our growing Data Science team. You will develop and deploy cutting-edge machine learning models and advanced data analytics solutions to solve real-world problems. You will collaborate with cross-functional teams (Product, Data Platform, DevOps, Software Engineering, etc.) to extract meaningful insights from data, develop scalable machine learning solutions, and help drive data-informed decision-making. PRIMARY ACCOUNTABILITIES - Design, develop, and deploy ML solutions ranging from traditional ML applications (classification, clustering, recommendations) to LLM-based systems, including document parsing, data extraction, RAG pipelines, and LLM agents. - Write clean, maintainable, production-quality Python code that integrates smoothly with existing engineering and deployment infrastructure. - Work with large datasets to clean, preprocess, and analyze data, ensuring data quality and integrity. - Implement and optimize algorithms using best practices in machine learning, deep learning, and statistical analysis. - Collaborate with business stakeholders to understand requirements and deliver data-driven solutions that provide actionable insights. - Develop and maintain scalable pipelines and infrastructure for data processing and model training, versioning, deployment, and monitoring. - Evaluate the performance of machine learning models, including LLM-specific evaluation approaches, and tune models for optimal performance. - Communicate findings, insights, and model performance to both technical and non-technical audiences. - Continuously stay updated on the latest trends, technologies, and best practices KNOWLEDGE, SKILLS AND ABILITIES Minimum Required Qualifications - Bachelor's or Master's degree in Computer Science, Data Science, Statistics, Mathematics, or a related field. or related experience. - Bachelor with 5+ years of relevant experience - Master or higher with 3+ years of relevant experience - Fluent in Python (3+ years of coding experience) - Strong software development practices in Python, including writing maintainable, testable, production-ready code. - Solid understanding of LLM architectures and Generative AI. - Hands-on experience building and evaluating RAG pipelines. - Experience with LLM orchestration frameworks (LangChain, LlamaIndex, or similar)Proficiency in machine learning libraries such as Scikit-learn and PyTorch; and fundamental libraries such as NumPy and Pandas - Familiarity with cloud platforms (e.g., AWS, GCP, Azure) and containerization tools (e.g., Docker). - Strong understanding of model evaluation metrics across traditional ML (e.g., accuracy, precision, recall, F1) and LLM-based systems (e.g., faithfulness, answer relevancy, hallucination detection), including approaches for evaluating non-deterministic outputs. - Experience with model management tools such as MLFlow and the model development life cycle. - Experience with version control tools such as Git. - Proficiency in adapting SDLC best practices for code development and testing. - Excellent problem-solving skills, analytical thinking, and the ability to work in a fast-paced environment. - Strong communication skills and the ability to explain complex technical concepts to non-technical stakeholders. Behavioral - Collaborator: work effectively with others, including domain experts, engineers, and business stakeholders - Inquisitive: desire to ask questions and get a deeper understanding of issues - Innovative: ability to imagine new analytical solutions to any problem - Confident: able to challenge perceptions and biases of individuals at every level of the organization. - Curious: stays abreast of current and upcoming technologies and tools - Business-oriented: solid understanding of business requirements and vernacular Preferred Qualifications - Familiarity with optimizing, deploying and scaling automated training pipelines of transformer-based models. - Familiarity with distributed training techniques and GPU-accelerated computing. - Familiarity with classical NLP approaches. - Experience implementing CI/CD pipelines for ML models for automating training, validation, monitoring, and scalable deployment. - Experience with integrating and deploying AWS AI/ML services. - Experience with Databricks - Experience in Health Care data SALARY, BENEFITS AND PERKS - Competitive total compensation package - Medical and dental coverage at no premium cost for employees - 401(k) and profit-sharing retirement plans - Flexible spending accounts - Paid time off (PTO) - Company-paid holidays - Gender-neutral parental leave - Bereavement and pet leave - Continuing education and professional accreditation sponsorship - Life and AD&D insurance - Short- and long-term disability - Employee assistance program - Mental health support program - Additional perks WORK ENVIRONMENT This is a remote role; however, we only operate in the following states: AZ, CA, CO, FL, GA, IL, IN, MA, MI, MN, MO, NJ, NY, NC, OH, PA, TX and WI. Reflected below is the base salary range offered for this position. Actual salaries may vary depending on factors including but not limited to academic achievements, skills and experience. The range listed is just one component of the compensation package offered to candidates. - $100,700 - $167,800 Our Values in Action: How We CARE We live our values daily through four commitments: - Connect: Collaborate selflessly to support others, advance ideas, and solve problems using critical thinking. - Act: Bring integrity and respect to every interaction—no exceptions. - Reach: Commit to continuous learning and knowledge sharing that strengthens teams and clients. - Embrace: Foster inclusion and belonging so everyone can thrive and contribute. SullivanCotter Holdings (Clinician Nexus, SCH Services, SullivanCotter) is an Equal Employment Opportunity/Affirmative Action employer, and all qualified applicants will receive consideration for employment without regard to race, color, religion, sex, age, national origin, protected veteran status, disability status, sexual orientation, gender identity or expression, marital status, genetic information, or any other characteristic protected by law or marital status.

• Work end-to-end on new Agentic AI Agents • Contribute to UXD, infrastructure, and machine learning challenges • Sole ownership of projects requiring iterative problem solving • Work directly with product managers to address commercial problems • Evaluate own work with established data frameworks • Consider long-term team direction for engineering capabilities