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AI/Machine Learning Infrastructure Engineer AI/Machine Learning Infrastructure Engineer Location: San Francisco, CA or New York City, NY Reports to: Head of Data Intelligence About Us We are a prestigious venture capital fund with offices in San Francisco and New York City, dedicated to identifying and nurturing transformative technology startups. Our Data Intelligence Team collaborates closely with our Investment Team to leverage data-driven insights, empowering smarter investment decisions and portfolio management. We are seeking a talented Machine Learning Infrastructure Engineer to build and scale the infrastructure that powers our cutting-edge machine learning initiatives. No prior financial services or venture capital experience is required —we value technical expertise and a passion for building impactful systems. Role Overview As a Machine Learning Infrastructure Engineer, you will design, develop, and maintain robust, scalable systems to support the deployment and operation of machine learning models. You will work at the intersection of data engineering, machine learning, and software development, enabling our Data Intelligence Team to deliver actionable insights to the Investment Team. This role requires a deep understanding of ML workflows, cloud infrastructure, and collaboration with cross-functional teams to drive innovation in investment strategies. Key Responsibilities ML Pipeline Development: Design and implement scalable, automated machine learning pipelines for data preprocessing, model training, deployment, and monitoring. Infrastructure Management: Build and maintain cloud-based infrastructure (e.g., AWS, GCP, or Azure) to support ML workloads, ensuring high availability, performance, and cost efficiency. Model Deployment: Deploy and optimize machine learning models in production environments, ensuring low-latency inference and seamless integration with investment analytics platforms. Collaboration: Partner with data scientists, investment analysts, and software engineers to translate business requirements into technical solutions that enhance investment decision-making. Performance Optimization: Optimize ML workflows for speed, scalability, and resource efficiency, including distributed computing and parallel processing. Monitoring and Maintenance: Implement monitoring tools and alerting systems to ensure the reliability and performance of ML systems in production. Security and Compliance: Ensure data security, privacy, and compliance with relevant regulations (e.g., GDPR, CCPA) in all ML infrastructure components. Innovation: Stay abreast of emerging tools, frameworks, and methodologies in ML infrastructure and propose innovative solutions to enhance team capabilities. Qualifications Education: Bachelor’s or Master’s degree in Computer Science, Engineering, or a related field. Experience: 3+ years of experience in machine learning infrastructure, data engineering, or a related role. Proven track record of building and deploying ML models in production environments. Experience with cloud platforms (AWS, GCP, or Azure) and containerization technologies (Docker, Kubernetes). Technical Skills: Proficiency in programming languages such as Python, Go, or Java. Expertise in ML frameworks (e.g., TensorFlow, PyTorch, Scikit-learn) and data processing tools (e.g., Apache Spark, Airflow). Strong knowledge of CI/CD pipelines, version control (Git), and infrastructure-as-code (e.g., Terraform, CloudFormation). Familiarity with database systems (SQL, NoSQL) and data orchestration tools. Soft Skills: Strong problem-solving skills and ability to work in a fast-paced, collaborative environment. Excellent communication skills to translate technical concepts to non-technical stakeholders, including the Investment Team. Preferred but not required: Experience with MLOps tools (e.g., MLflow, Kubeflow, or SageMaker). Why Join Us? Impact: Play a pivotal role in shaping investment strategies through data-driven insights. Innovation: Work with cutting-edge ML technologies in a dynamic, high-impact environment. Collaboration: Join a collaborative team of top-tier data scientists, engineers, and investment professionals. Growth: Access opportunities for professional development and growth in a prestigious VC fund. Location: Enjoy the vibrant tech ecosystems of San Francisco or New York City with a hybrid work model. Seniority level Seniority level Mid-Senior level Employment type Employment type Full-time Job function Job function Engineering and Information Technology Industries Human Resources Referrals increase your chances of interviewing at Almond Partners (Buy-Side Executive Search) by 2x Sign in to set job alerts for “Machine Learning Engineer” roles. 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