Machine Learning Ops Intern

Job Description: Machine Learning Ops Intern

Blockhouse is focused on real-time machine learning and data engineering, building scalable infrastructure for high-frequency ML models that redefine how organizations extract actionable insights from data. Our systems drive the future of real-time analytics, leveraging cutting-edge technology to deploy machine learning pipelines with sub-second level response times. If you’re passionate about building the future of MLOps and want to work with a world-class team, this is your opportunity.

Role Description:

We are seeking the next generation of MLOps engineering talent. As an MLOps Engineering Intern, you will help architect and scale real-time machine learning systems that push the boundaries of what’s possible. You’ll be building mission-critical dashboards and analytics infrastructure for high-frequency models, ensuring seamless deployment, monitoring, and performance optimization in real-time environments.

This role is not for the average engineer. You will be collaborating with data engineers and machine learning scientists to build systems that scale to millions of data points per second, using technologies to support high-throughput, low-latency data streaming and querying. Your work will directly shape the future of real-time machine learning at scale.

Key Responsibilities:

1. Build Real-Time Dashboards: Develop, design, and optimize real-time dashboards to visualize the performance of high-frequency machine learning models, providing instant insights and decision-making capabilities.

2. High-Frequency Model Pipelines: Build and maintain machine learning pipelines that enable continuous, low-latency model training, serving, and monitoring at scale.

3. Advanced Data Integration: Design and implement seamless data integration between high-frequency models and real-time streaming architectures using Redpanda (or similar technologies) for event-driven processing and ClickHouse (or similar technologies) for OLAP analytics.

4. Real-Time Monitoring & Optimization: Create highly resilient monitoring systems with real-time feedback loops to ensure that machine learning models perform at optimal levels. Automate retraining and performance alerts using state-of-the-art monitoring tools.

5. Cloud Infrastructure & CI/CD Automation: Develop automated CI/CD pipelines for the deployment and lifecycle management of machine learning models on cloud-native infrastructure (AWS, GCP). Focus on creating scalable, robust systems that can handle real-time loads.

6. Collaborate with Elite Teams: Work closely with a team of world-class engineers and data scientists to ensure seamless collaboration between data pipelines, machine learning model development, and production deployments.

What You’ll Need:

• 1+ Years of MLOps Experience: Demonstrable experience in building and scaling machine learning pipelines with a focus on high-frequency data and real-time model performance.

• Mastery of Real-Time Systems: Expertise in building and optimizing real-time dashboards, streaming data pipelines, and infrastructure for high-throughput, low-latency environments. Hands-on experience with ClickHouse and Redpanda (or similar technologies) is critical to succeed in this role.

• Proficiency in Python & ML Frameworks: Deep expertise in Python and machine learning libraries such as TensorFlow, PyTorch, and Scikit-learn. Ability to deploy these models in real-time production environments.

• Infrastructure & Cloud Expertise: Mastery of cloud platforms like AWS or GCP, with experience deploying and managing machine learning models using tools like SageMaker, Lambda, EKS, or similar.

• CI/CD & Automation: Extensive experience building automated CI/CD pipelines for model deployment, using tools such as Jenkins, GitLab CI, or equivalent.

• Monitoring at Scale: Experience with monitoring and alerting tools like Prometheus, Grafana, CloudWatch, or similar, and the ability to implement real-time monitoring for machine learning systems.

• World-Class Collaboration: A collaborative mindset with the ability to work across teams and drive results in a fast-paced, high-performance environment.

Ideal Candidate Profile:

• Experience in building and optimizing dashboards for real-time machine learning models using Next.js or similar frameworks.

• Knowledge of event-driven architectures, including tools like Redpanda or Kafka, for processing millions of events per second.

• Familiarity with feature stores and scalable feature engineering processes integrated with real-time OLAP systems like ClickHouse.

• Understanding of advanced data security protocols and infrastructure cost optimization strategies.

• Problem-Solving Skills: Strong analytical and problem-solving skills with a keen attention to detail.

• Self-Motivated: Self-driven and capable of working independently with minimal supervision.

This is a part time role (20 hours / week). Candidates must be able to attend standup meetings at 10am EST and be available during the following working hours. This role is not for winter / summer positions - we are looking to fill this role as soon as possible.

Why You Should Join Us:

• Innovative Environment: Be at the forefront of financial innovation at a company integrating advanced quantitative techniques with traditional financial models.

• Expert Team: Collaborate with some of the brightest minds in the industry in an environment that values bold ideas and radical solutions to complex problems.

• Professional Growth: Thrive in a vibrant company culture that promotes career development, continuous learning, and work-life balance.

• Cutting-Edge Projects: Work on transformative projects that directly impact the future of financial technology.

• Compensation: Equity-only compensation. In-person perks for NYC-based employees include daily free lunch and weekly company bonding events.

If you are passionate about software development and eager to apply your skills to drive innovation in financial technology, join us at Blockhouse. Together, we will redefine the future of trade execution and cost analysis.