Role Description The Senior Machine Learning Engineer is responsible for developing and implementing machine learning solutions that enhance clinical decision-making, patient outcomes, and operational efficiency across Evergreen's telehealth platform. This role combines deep technical expertise with healthcare domain knowledge to design, deploy, and optimize production-ready AI systems that are accurate, explainable, and compliant with regulatory requirements. Working closely with Engineering, Clinical Leadership, and Product stakeholders, the Senior Machine Learning Engineer will help translate complex clinical and business challenges into scalable machine learning solutions. The ideal candidate is a hands-on engineer who thrives in a fast-paced startup environment, exercises sound technical judgment, and is passionate about building AI capabilities that deliver measurable value while maintaining the highest standards of patient safety and quality. Note on Scope: The Senior Machine Learning Engineer serves as the technical owner of Evergreen's machine learning and predictive analytics capabilities. This role is responsible for designing, deploying, monitoring, and continuously improving the models that power clinical decision support, patient risk stratification, adherence forecasting, dosage optimization, and agent-driven workflows. Working closely with Engineering, Clinical Leadership, and Product stakeholders, the Senior Machine Learning Engineer ensures that AI systems are scalable, explainable, compliant, and aligned with patient safety requirements. The role influences machine learning architecture, model governance, and technical standards while serving as a key contributor to Evergreen's long-term AI strategy. Responsibilities - Design, training, validation, and monitoring of the core predictive and state-transition models powering the clinical product. - Production ML pipelines and retraining cadence, including drift detection and rollback. - Model explainability and validation work needed to support clinical and regulatory review. - Build the evaluation and validation framework for all agent-driven clinical recommendations, including safety guardrails, confidence thresholds, and human-in-the-loop escalation triggers for the Clinical Protocol Agent. - Develop patient risk stratification models for adherence prediction, adverse event likelihood, dosage titration optimization, and churn/dropout risk using clinical, behavioral, and engagement signals. - Design predictive models that support clinical decision-making while maintaining explainability and regulatory compliance. - Collaborate with clinical stakeholders to translate protocols, treatment pathways, and pharmacy domain expertise into model features, training labels, and validation criteria. - Implement and manage the predictive analytics pipeline on AWS, including Amazon Forecast (DeepAR+) for time-series clinical predictions, S3 Vectors for embedding-based patient similarity and retrieval, and Bedrock for agent inference. - Design and build the RAG architecture that grounds agent responses in clinical protocols, formulary data, and operational knowledge sources. - Contribute to machine learning architecture decisions and establish technical standards that support scalability, reliability, and maintainability. - Partner with Engineering leadership to evaluate emerging AI technologies and recommend solutions aligned with business objectives and patient safety requirements. - Own model lifecycle management, including training pipelines, feature stores, model versioning, A/B testing, drift detection, and retraining triggers in production. - Establish model monitoring and alerting, including prediction quality dashboards, distribution shift detection, and automated alerts when performance degrades below established thresholds. - Build explainability layers for clinical recommendations to support provider trust, auditability, and regulatory requirements. - Ensure machine learning systems meet governance, compliance, and documentation standards appropriate for healthcare environments. - Serve as a senior technical resource for AI and machine learning initiatives across the organization. - Provide technical guidance and mentorship to engineers contributing to machine learning, AI, and analytics initiatives. - Partner with Clinical Leadership, Engineering, Product, and Operations teams to ensure models deliver measurable business and patient outcomes. - Work with DevOps/AgentOps resources to ensure all ML decisions are logged, reproducible, and auditable for regulatory review. Qualifications - MS or PhD in a quantitative discipline: Math, Econometrics, Physics, Computational Engineering, or a related field. - Excellent Python skills, with production experience in a modern ML framework such as PyTorch or TensorFlow. - Experience developing and deploying production ML models, not just research or notebooks. - Experience building statistical or neural network models for prediction, classification, or state-transition problems. - Experience with MLOps: model versioning, automated retraining, drift detection, and production monitoring. Preferred Qualifications - Experience developing ML models in a healthcare, clinical, biotech, or pharma context. - Experience with structured clinical data, including FHIR, claims, or EHR data. - Experience building explainable models for regulated or safety-sensitive domains. - Published research, patents, or an advanced thesis involving applied statistical or computational modeling. Key Performance Metrics - Model Performance Accuracy: Achievement of target accuracy, precision, recall, and calibration metrics across production models. - Delivery & Deployment: Successful delivery and deployment of machine learning initiatives against roadmap commitments. - AI System Uptime & Reliability: Availability and operational performance of machine learning services and inference pipelines. - Model Reliability: System uptime, performance monitoring, and timely resolution of model drift or degradation. - Model Drift Detection & Resolution: Timely identification and remediation of model performance degradation. Requirements - Applicants must be authorized to work for ANY employer in the U.S. We are unable to sponsor or take over sponsorship of an employment Visa at this time. - Evergreen Telehealth is an equal opportunity employer. All qualified applicants will receive consideration for employment without regard to race, color, religion, sex, sexual orientation, gender identity, national origin, disability, or veteran status.

• Design and scale production ML systems for LLM-based applications. • Build training and evaluation pipelines for continuous model improvement. • Fine-tune foundation models using modern adaptation techniques such as LoRA, QLoRA, SFT and DPO. • Optimize inference performance, latency and GPU utilization. • Develop high-quality training datasets and evaluation frameworks. • Own deployment, monitoring and reliability of ML services in production. • Help define engineering standards while mentoring a small, high-calibre ML team.

Role Description We are looking for a Machine Learning Engineer - RL to design, train, and deploy RL-based systems for high-impact decision-making problems where supervised learning alone is insufficient. The role requires deep familiarity with modern reinforcement learning algorithms, simulation environments, reward modeling, and the engineering complexity of training and evaluating policies at scale. The ideal candidate has both research depth and engineering pragmatism, with experience taking RL solutions out of the lab and into production where stability, safety, and ongoing improvement are critical. Key Responsibilities - Design and implement reinforcement learning solutions for sequential decision-making problems in real and simulated environments. - Develop, calibrate, and maintain simulation environments suitable for large-scale agent training. - Implement and evaluate modern RL algorithms including policy gradient, actor-critic, off-policy, and offline RL methods. - Engineer reward functions and shaping strategies that align agent behavior with desired outcomes and safety constraints. - Apply offline RL and imitation learning techniques where exploration is costly or unsafe. - Use RLHF, DPO, and related techniques for fine-tuning large language models when relevant. - Build scalable training infrastructure for distributed RL, including efficient experience collection and replay systems. - Optimize training stability and sample efficiency through algorithmic and engineering improvements. - Design rigorous evaluation protocols, including out-of-distribution and adversarial test cases. - Implement safety mechanisms such as constraint enforcement, conservative policies, and human-in-the-loop oversight. - Collaborate with applied scientists and product teams to identify high-value RL use cases. - Monitor deployed policies and models in production for drift, regression, and unintended behaviors, building the alerting and dashboards that surface issues before they meaningfully affect users. - Document methodology, design decisions, and operational characteristics for internal stakeholders. - Stay current with RL research and translate promising techniques into production-ready solutions. Qualifications - Master’s or PhD in Computer Science, Machine Learning, or a related field; or equivalent applied experience. - Six or more years of combined RL research and engineering experience. - Strong proficiency in Python and modern deep learning frameworks. - Hands-on experience with at least one major RL library or in-house RL stack. - Solid understanding of probability, optimization, and the theoretical foundations of RL. - Experience designing and tuning reward functions in non-trivial environments. - Familiarity with simulation environments and large-scale experience collection. - Experience training neural network policies on GPU clusters. - Strong written and verbal communication skills. - Track record of shipping or publishing impactful RL work. Preferred Qualifications - Experience with RLHF for large language models. - Familiarity with multi-agent RL or hierarchical RL. - Exposure to robotics, control systems, or autonomous driving. - Publications in RL or related research venues. - Open-source contributions to RL libraries or environments. How to Apply Would you like to know more about this opportunity? For immediate consideration, please send your resume to [email protected] or contact us at (908) 676-4399. Learn more about Bright Vision Technologies at www.bvteck.com . Equal Employment Opportunity (EEO) Statement Bright Vision Technologies (BV Teck) is committed to equal employment opportunity (EEO) for all employees and applicants without regard to race, color, religion, sex, sexual orientation, gender identity or expression, national origin, age, genetic information, disability, veteran status, or any other protected status as defined by applicable federal, state, or local laws. This commitment extends to all aspects of employment, including recruitment, hiring, training, compensation, promotion, transfer, leaves of absence, termination, layoffs, and recall. BV Teck expressly prohibits any form of workplace harassment or discrimination. Any improper interference with employees' ability to perform their job duties may result in disciplinary action up to and including termination of employment.

• Own and improve the machine learning models and systems that power the Home feed, including the Shortcuts experience. • Design, build, and ship personalized recommendations that serve millions of Spotify listeners globally. • Build content recommendation systems for emerging agentic and AI-powered user experiences. • Train, fine-tune, evaluate, and optimize large language models using techniques such as supervised fine-tuning (SFT), distillation, and parameter-efficient training approaches. • Partner closely with product managers, engineers, data scientists, and designers to define and execute experimentation strategies. • Drive A/B testing, monitoring, model evaluation, and continuous optimization of recommendation quality, reliability, and cost efficiency. • Improve ML platform capabilities, data pipelines, and production systems that support personalization at Spotify scale.