This description is a summary of our understanding of the job description. Click on 'Apply' button to find out more. Role Description This role offers the opportunity to design, build, and deploy large-scale machine learning systems that drive personalized content discovery, search relevance, and intelligent ad optimization. The Senior Machine Learning Engineer will work across the full ML lifecycle, impacting millions of daily users. - Design, implement, and deploy production-grade ML models and systems at scale for content ranking, recommendation, and ad optimization. - Own the full ML lifecycle, including problem definition, feature engineering, training, evaluation, deployment, and monitoring. - Build scalable data and model pipelines with reliability, observability, and automated retraining. - Work with large-scale datasets to improve predictions, search relevance, and user/content understanding. - Research and apply advanced ML/AI techniques, including deep learning, graph-based models, transformers, and LLM evaluation. - Collaborate cross-functionally with Product, Data Science, Infrastructure, and Engineering teams to deliver impactful ML solutions. - Contribute to technical strategy, architecture decisions, and the long-term ML roadmap. Qualifications - 3–5+ years of experience building, deploying, and operating machine learning systems in production. - Strong programming skills in Python, Java, Go, or similar languages, with solid software engineering fundamentals. - Proficiency in modern ML frameworks such as PyTorch or TensorFlow. - Experience designing scalable ML pipelines, data processing systems, and model serving infrastructure. - Familiarity with recommender systems, search/ranking systems, advertising/auction systems, or large-scale representation learning. - Knowledge of distributed systems, big data processing (Spark, Kafka, Ray, Airflow, BigQuery, Redis), and low-latency real-time environments. - Experience with feature engineering, model optimization, and production monitoring. - Strong problem-solving skills and the ability to translate ambiguous business or product problems into ML solutions. - Preferred: experience with LLM/Generative AI techniques, advanced ML research, or prior work in large-scale user-facing platforms. Requirements - 3–5+ years of experience building, deploying, and operating machine learning systems in production. - Strong programming skills in Python, Java, Go, or similar languages, with solid software engineering fundamentals. - Proficiency in modern ML frameworks such as PyTorch or TensorFlow. - Experience designing scalable ML pipelines, data processing systems, and model serving infrastructure. - Familiarity with recommender systems, search/ranking systems, advertising/auction systems, or large-scale representation learning. - Knowledge of distributed systems, big data processing (Spark, Kafka, Ray, Airflow, BigQuery, Redis), and low-latency real-time environments. - Experience with feature engineering, model optimization, and production monitoring. - Strong problem-solving skills and the ability to translate ambiguous business or product problems into ML solutions. - Preferred: experience with LLM/Generative AI techniques, advanced ML research, or prior work in large-scale user-facing platforms. Benefits - Competitive salary range of $185,800–$303,400 USD. - Equity opportunities through restricted stock units. - Comprehensive healthcare coverage including medical, dental, and vision. - 401(k) program with employer match. - Flexible vacation, paid volunteer time off, and generous parental leave. - Global benefit programs supporting professional development, caregiving, mental health, and family planning. - Remote-friendly, inclusive, and collaborative work environment.

This description is a summary of our understanding of the job description. Click on 'Apply' button to find out more. Role Description As a Senior Machine Learning Engineer, you will own the end to end ML lifecycle at Button, from the data and feature pipelines that feed models, through training and evaluation workflows, to deployment, scoring, monitoring, and retraining. You will work closely with product managers, data scientists, and software engineers to translate ambiguous business problems into reliable production ML systems that integrate with our platform and power real product decisions. You will help build the infrastructure and patterns that allow models to move quickly from research to production while meeting real world requirements for latency, scalability, cost efficiency, reproducibility, and safety. This role works closely with our Data Teams and plays a central role in how machine learning powers Button’s commerce and monetization products. - Own the full ML lifecycle including feature pipelines, training workflows, model deployment, inference services, monitoring, and retraining. - Design and build reliable data and feature pipelines, including feature store patterns that support reproducible training and consistent features across training, batch scoring, and online inference. - Build and optimize machine learning models including regression, classification, ranking, and recommender systems. - Implement and manage batch scoring pipelines and online inference services with clear performance, reliability, and latency standards. - Partner with data scientists to operationalize models and build the tooling needed to run consistent evaluation, experimentation, and model iteration. - Collaborate with software engineers to ensure smooth integration of models into production services and APIs. - Establish observability for ML systems including monitoring of data freshness, feature drift, model performance, and pipeline health. - Design systems that support rapid experimentation and safe rollout of new models. - Document architecture clearly, establish best practices for ML engineering at Button, and mentor teammates through thoughtful code reviews and design discussions. - Contribute to the design of decisioning systems that power ranking, recommendations, and commerce optimization across Button’s platform. Qualifications - Write clear, maintainable code with strong software engineering practices including testing, documentation, debugging, and thoughtful system design. - Have experience building and operating production machine learning systems rather than only training models. - Understand the full ML lifecycle including feature generation, training pipelines, deployment strategies, and monitoring. - Have practical experience designing scalable data pipelines and feature generation workflows. - Have experience building or working with feature pipelines or feature stores that support both training and online inference. - Think deeply about reliability, scalability, latency, and cost efficiency when building ML systems. - Are comfortable working in cloud environments, especially AWS. Familiarity with Amazon SageMaker, Redis, Spark, streaming systems, or distributed data processing frameworks is a plus. - Have experience with machine learning frameworks such as PyTorch, TensorFlow, or scikit-learn. - Enjoy collaborating closely with engineers, data scientists, and product managers and can clearly communicate technical tradeoffs and design decisions. - Are comfortable working in ambiguous problem spaces and translating product questions into measurable ML solutions. Requirements - 5+ years of professional experience in machine learning engineering, software engineering, data engineering, or similar roles. - Fluency with Python and SQL. - Proven experience designing, building, and operating data pipelines at scale. - Hands on experience deploying and maintaining machine learning models in production environments. - Experience working in cloud environments, especially AWS. - Familiarity with orchestration and data modeling tools such as Airflow, dbt, or similar systems. - Experience building ranking, recommendation, or decisioning systems is a plus. Benefits - RemotePlus workplace, blending “work from anywhere” with in-person collaboration. - Salary range: $153,000 - $198,000. - 401(k) plan with 3% annual contribution from the company. - Unlimited time off (including birthdays off) and periodic Mental Health Weeks. - 100% premium coverage for health, vision, and dental insurance for employees; 75% for dependents. - Complimentary memberships to One Medical. - Monthly stipend for mobile phone/internet and an annual lifestyle stipend. - “All Access” memberships to WeWork in select markets and regular coworking days and social events.

Requisition Number: 2350261 Optum Tech is a global leader in health care innovation. Our teams develop cutting-edge solutions that help people live healthier lives and help make the health system work better for everyone. From advanced data analytics and AI to cybersecurity, we use innovative approaches to solve some of health care's most complex challenges. Your contributions here have the potential to change lives. Ready to build the next breakthrough? Join us to start Caring. Connecting. Growing together. You'll enjoy the flexibility to work remotely* from anywhere within the U.S. as you take on some tough challenges. For all hires in the Minneapolis or Washington, D.C. area, you will be required to work in the office a minimum of four days per week. Primary Responsibilities: - Design and build AI-powered features using NLP/LLMs and classical ML for provider chat and voice workflows - Deliver end to end: data preparation, modeling/experimentation, evaluation, API integration, deployment, and monitoring in cloud environments - Implement responsible AI guardrails (content safety, PHI/PII controls, prompt hygiene, evaluation suites) and collaborate with governance/review partners - Develop and maintain web services and APIs that expose ML capabilities; write clear documentation and runbooks - Apply MLOps practices (CI for ML, model registry, reproducible experiments) and contribute to on call/operational support with mentorship - Collaborate with product, architecture, security, data, and platform teams to align with enterprise standards and deliver measurable outcomes - Continuously improve code quality, test coverage, latency, reliability, and cost per inference You'll be rewarded and recognized for your performance in an environment that will challenge you and give you clear direction on what it takes to succeed in your role as well as provide development for other roles you may be interested in. Required Qualifications: - 3+ years of total software engineering experience - 2+ years of experience with Python programming - 2+ years of experience building and integrating model backed APIs/services (Python/Node/Java) into production systems - 1.5+ years of hands-on in AI/ML (e.g., NLP, GenAI, predictive modeling, or recommendations) - 1.5+ years of experience working with AWS cloud and components containers/serverless, IAM basics, storage/networking fundamentals, logging/metrics - 1+ years of experience in implementing security, access control, monitoring/alerting, and foundational resiliency/DR concepts for cloud applications - 1+ years of experience with versioning control, code reviews, automated testing, and CI/CD Preferred Qualifications: - Bachelors degree in Computer Science or related field - Exposure to healthcare data/processes *All employees working remotely will be required to adhere to UnitedHealth Group's Telecommuter Policy. Pay is based on several factors including but not limited to local labor markets, education, work experience, certifications, etc. In addition to your salary, we offer benefits such as, a comprehensive benefits package, incentive and recognition programs, equity stock purchase and 401k contribution (all benefits are subject to eligibility requirements). No matter where or when you begin a career with us, you'll find a far-reaching choice of benefits and incentives. The salary for this role will range from $98,547 to $175,978 annually based on full-time employment. We comply with all minimum wage laws as applicable. Application Deadline: This will be posted for a minimum of 2 business days or until a sufficient candidate pool has been collected. Job posting may come down early due to volume of applicants. At UnitedHealth Group, our mission is to help people live healthier lives and make the health system work better for everyone. We believe everyone-of every race, gender, sexuality, age, location and income-deserves the opportunity to live their healthiest life. Today, however, there are still far too many barriers to good health which are disproportionately experienced by people of color, historically marginalized groups and those with lower incomes. We are committed to mitigating our impact on the environment and enabling and delivering equitable care that addresses health disparities and improves health outcomes - an enterprise priority reflected in our mission. UnitedHealth Group is an Equal Employment Opportunity employer under applicable law and qualified applicants will receive consideration for employment without regard to race, national origin, religion, age, color, sex, sexual orientation, gender identity, disability, or protected veteran status, or any other characteristic protected by local, state, or federal laws, rules, or regulations. UnitedHealth Group is a drug - free workplace. Candidates are required to pass a drug test before beginning employment.

This description is a summary of our understanding of the job description. Click on 'Apply' button to find out more. Role Description This role offers the opportunity to design and deploy cutting-edge AI and machine learning solutions that drive critical decision-making processes in large-scale digital financial systems. The Machine Learning Engineer will work on applied research projects, building and optimizing advanced architectures such as Transformers, GNNs, and multimodal models for production environments. You will collaborate with cross-functional teams to translate complex research into scalable, efficient, and cost-effective solutions that deliver measurable business impact. The position emphasizes autonomy, technical leadership, and innovation while contributing to the growth and expertise of the AI function. Ideal candidates thrive on solving ambiguous challenges, mentoring peers, and shaping the future of AI-driven financial products. The environment supports high-impact work in a collaborative and forward-thinking team. - Lead and execute applied research initiatives, focusing on designing architectures deployable across critical business use cases. - Solve complex, ambiguous ML problems, coordinating with Data, Infrastructure, and Product teams to deliver innovative solutions. - Bridge research and production by ensuring models are optimized for latency, interpretability, scalability, and cost-efficiency. - Collaborate cross-functionally to integrate AI solutions into downstream decision systems. - Establish technical standards for experimentation, model evaluation, and code quality within the AI team. - Mentor senior engineers and researchers, providing guidance on deep learning methodologies and best practices. - Contribute to internal thought leadership through papers, presentations, or research collaborations aligned with strategic goals. Qualifications - 5–7+ years of experience in applied AI/ML with proven success deploying research-driven systems into production. - Deep expertise in Deep Learning architectures, including Transformers, Graph Neural Networks, and multimodal models. - Strong programming skills in Python and proficiency with ML frameworks like PyTorch, JAX, or TensorFlow. - Solid understanding of MLOps practices and constraints for production-scale models. - Experience with large-scale experimentation, A/B testing, and problem formulation in complex or ambiguous datasets. - Excellent communication skills to convey technical concepts to both technical and non-technical stakeholders. - Ability to collaborate across teams and provide mentorship to peers and junior engineers. Benefits - Competitive salary with potential equity opportunities. - Comprehensive medical, dental, and vision insurance. - Life insurance and AD&D coverage. - Extended maternity and paternity leave programs. - Learning and development programs, including internal courses and language learning initiatives. - Mental health and wellness support. - 401(k) retirement plans and health savings options. - Work-from-home allowance and relocation assistance if applicable.