Role Description We are seeking a Mid-Level AI/ML Engineer to design, build, and deploy machine learning models and end-to-end ML pipelines that drive business value. In this role, you will independently own the development of ML solutions from experimentation through production deployment, collaborate with cross-functional teams, and contribute to the maturity of our MLOps practices. The ideal candidate combines strong ML fundamentals with practical engineering skills and a growing ability to make independent technical decisions. - Design, develop, and deploy machine learning models for classification, regression, NLP, computer vision, or recommendation use cases. - Build and maintain end-to-end ML pipelines including data ingestion, feature engineering, model training, validation, and serving. - Implement MLOps practices including automated training pipelines, experiment tracking, model versioning, and reproducibility. - Deploy models to production using containerization (Docker, Kubernetes) and cloud-native services. - Monitor model performance in production, implement data drift detection, and manage model retraining workflows. - Collaborate with data engineers, software engineers, and product teams to integrate ML solutions into applications and services. - Optimize model performance for latency, throughput, and resource efficiency in production environments. - Write production-quality code with proper testing, logging, error handling, and documentation. - Participate in technical design discussions and contribute to architectural decisions for ML systems. - Mentor junior engineers and contribute to team knowledge-sharing and best practices. Qualifications - Bachelor’s or Master’s degree in Computer Science, Mathematics, Statistics, Data Science, or a related technical field. - 3–5 years of professional experience in machine learning engineering, applied ML, or a closely related role. - Strong proficiency in Python and hands-on experience with ML frameworks such as TensorFlow, PyTorch, or scikit-learn. - Experience building and deploying ML pipelines in production environments. - Working knowledge of MLOps tools and practices including MLflow, Kubeflow, Airflow, or similar orchestration frameworks. - Experience with cloud platforms (AWS SageMaker, Azure ML, or GCP Vertex AI) for training, deployment, and serving. - Proficiency in SQL and experience working with large-scale datasets. - Experience with Docker, Kubernetes, and CI/CD pipelines for ML workflows. - Solid understanding of model evaluation, hyperparameter tuning, and feature engineering techniques. - Familiarity with REST APIs and microservices architecture for model serving. Preferred Qualifications - Experience with deep learning architectures such as Transformers, CNNs, or RNNs. - Familiarity with feature stores (Feast, Tecton) and data versioning tools (DVC, LakeFS). - Experience with model monitoring and observability tools (Evidently AI, WhyLabs, or Prometheus/Grafana). - Exposure to distributed training frameworks and GPU-accelerated computing. - Experience with A/B testing and experimentation frameworks for ML models. - Knowledge of data engineering tools such as Spark, Kafka, or dbt. Salary Range - US East/West Coast: $108,700 - $144,900 - US Remote: $92,400 - $123,200 Disclaimer: Actual compensation may vary based on individual qualifications, experience, and specific geographic location. These figures represent base salary ranges and do not include additional benefits or incentives. Benefits - Unlimited PTO. - Please ask us about our very generous parental leave, much above industry standards! - Entrepreneurial culture where pushing limits and taking risks is everyday business. - Open communication with management and company leadership. - Small, dynamic teams = massive impact. - Medical, Dental and Vision coverage for employees. - Access to Disability & Life insurance. - Mental health and wellbeing support. - Annual bonus program. - Employer Stock Purchase Program (ESPP). - Yearly Team building experiences. - Mentorship and sponsorship opportunities. - Manager resources and support. Company Description We are an equal opportunity employer. We celebrate diversity and are committed to creating an inclusive environment for all employees. All qualified applicants will receive consideration for employment without regard to race, color, religion, sex, sexual orientation, gender identity, national origin, disability, or any other protected characteristic.

• Design, implement, and maintain end-to-end ML pipelines covering training, validation, deployment, monitoring, and retraining — with a focus on production reliability and long-term maintainability. • Own and operate production ML infrastructure using Infrastructure as Code (IaC), making architectural tradeoffs and enforcing best practices. • Lead CI/CD practices for ML, including artifact/model versioning, promotion, rollout/rollback, and dev/test/prod parity. • Deploy and run ML/GenAI workloads on Azure using Azure App Service and Azure Container Apps, with monitoring via Application Insights. • Implement robust model observability: performance monitoring, data quality checks, drift detection, alerting, and dashboards. • Drive compute and cost optimization for training and inference (scaling policies, capacity planning, cost/performance tradeoffs). • Support GenAI operational needs, including LLM inference patterns, embeddings, and retrieval pipelines; enable hooks for evals/guardrails where required. • Ensure ML systems meet security and governance requirements (RBAC/least privilege, secrets management, audit logging, encryption, secure access patterns). • Partner with the Data & AI Architect to translate architecture standards into reusable pipeline templates and operational controls. • Collaborate with and mentor AI engineers; contribute to model development/experimentation as capacity allows.

Role Description We are seeking a Principal AI/ML Engineer to serve as a technical leader and architect for our organization’s AI/ML strategy, systems, and platforms. In this role, you will define the long-term technical vision for machine learning and MLOps, drive cross-team alignment on architecture and standards, solve the hardest technical problems, and ensure our ML capabilities are enterprise-grade, scalable, and forward-looking. The ideal candidate is a recognized technical authority who combines deep hands-on expertise with strategic thinking, organizational influence, and the ability to elevate the entire engineering team. Key Responsibilities - Define and own the long-term technical vision, architecture, and roadmap for the organization’s AI/ML platform and MLOps capabilities. - Serve as the senior technical authority on ML system design, providing guidance on architecture, tooling, and engineering standards across the organization. - Lead the design of foundational ML infrastructure including training platforms, feature stores, model registries, serving systems, and observability frameworks. - Establish and evangelize best practices for the full ML lifecycle: experimentation, development, testing, deployment, monitoring, governance, and retirement. - Drive cross-functional alignment between ML engineering, data engineering, platform engineering, product, and security teams. - Evaluate emerging technologies, frameworks, and research to inform strategic decisions on AI/ML tooling and approaches. - Solve complex, ambiguous, and high-impact technical problems that span multiple teams or systems. - Mentor and develop senior and staff-level engineers, fostering a culture of technical excellence and continuous improvement. - Represent the engineering organization in discussions with leadership, partners, and clients on AI/ML capabilities and strategy. - Define and enforce governance, compliance, and responsible AI practices across ML systems. - Drive cost optimization and efficiency across ML training, serving, and infrastructure. - Contribute to hiring, technical assessments, and the growth of the AI/ML engineering team. Qualifications - Master’s or PhD in Computer Science, Mathematics, Statistics, or a related field preferred. Equivalent professional experience is accepted. - 10+ years of professional experience in software engineering, ML engineering, or applied AI, with at least 5 years focused on production ML systems. - Deep expertise in designing and scaling production ML platforms, pipelines, and infrastructure. - Authoritative knowledge of MLOps principles, tools, and practices including CI/CD for ML, automated retraining, model governance, and observability. - Extensive experience with cloud-native ML services across AWS, Azure, or GCP, and infrastructure-as-code tools (Terraform, Pulumi, CloudFormation). - Strong understanding of distributed systems, data architecture, and scalable platform design. - Proven track record of driving technical strategy and influencing engineering direction at an organizational level. - Experience leading and mentoring senior engineers and building high-performing technical teams. - Exceptional communication skills with the ability to align technical and business stakeholders on complex topics. - Strong understanding of responsible AI, model fairness, explainability, security, and compliance requirements. Preferred Qualifications - Experience architecting LLM-based systems, RAG pipelines, agentic AI platforms, or conversational AI at enterprise scale. - Experience with MCP/tool-layer integrations for LLM-driven systems. - Deep familiarity with feature platforms, model serving at scale, and real-time inference architectures. - Track record of contributing to or leading industry standards, publications, or open-source projects in AI/ML. - Experience with enterprise AI governance frameworks and regulatory compliance (SOC2, HIPAA, GDPR). - Experience working across geographically distributed or offshore engineering teams. - Background in financial services, healthcare, or other highly regulated industries. Salary Range - US East/West Coast: $159,800 - $213,100 - US Remote: $135,900 - $181,200 Benefits - Unlimited PTO. - Very generous parental leave, much above industry standards. - Entrepreneurial culture where pushing limits and taking risks is everyday business. - Open communication with management and company leadership. - Small, dynamic teams = massive impact. - Medical, Dental and Vision coverage for employees. - Access to Disability & Life insurance. - Mental health and wellbeing support. - Annual bonus program. - Employer Stock Purchase Program (ESPP). - Yearly Team building experiences. - Mentorship and sponsorship opportunities. - Manager resources and support. Company Description We are an equal opportunity employer. We celebrate diversity and are committed to creating an inclusive environment for all employees. All qualified applicants will receive consideration for employment without regard to race, color, religion, sex, sexual orientation, gender identity, national origin, disability, veteran status, or any other protected characteristic.

• Work across the Campaign Monitor product to identify valuable opportunities in product and customer data, and turn them into predictive features that improve customer outcomes • Turn rich historical product and customer data into predictive features that improve customer outcomes • Identify high-impact opportunities for applied machine learning by analyzing product, behavioral, and content data, and translating ambiguous product questions into concrete ML use cases • Develop and deploy predictive machine learning models, including models for click-through rate, churn, recommendations, and related engagement signals • Design and build features and training datasets from structured product data, historical behavioral data, and content-derived signals • Own the applied ML lifecycle from data exploration and feature engineering through training, evaluation, deployment, monitoring, and iteration • Build production services and workflows for batch and real-time inference, with a pragmatic focus on reliability, maintainability, and speed to impact • Work hands-on in the codebase, contributing to backend systems and product workflows that consume predictions and recommendations • Partner closely with product, design, and engineering to turn customer needs into ML-driven product capabilities with measurable business impact • Establish pragmatic best practices for model evaluation, experimentation, monitoring, and continuous improvement • Help shape how applied machine learning is introduced into the product, while aligning with broader engineering architecture and delivery practices • Contribute to shared knowledge across the engineering organization to improve understanding and adoption of applied ML over time.