Principal ML Ops Engineer

Sep 05, 2023

New York City, United States

A market leader in credit intelligence, Reorg brings together journalists, financial analysts, legal analysts, technologists, and data scientists to collect and synthesize highly complex information into actionable intelligence. Since 2013, tens of thousands of professionals across hedge funds, investment banks, management consulting, and law firm verticals have come to rely on Reorg to make better, faster, and more confident decisions in pace with the fast-moving credit markets. For more information, visit:

Working at Reorg**

Consistent with our growth, Reorg hires innovators and trailblazers across the globe to drive our business and our incredible corporate culture alike. Our core values – Action OrientedCustomer First MindsetEffective Team Players, and Driven to Excel – define an organizational ethos that’s as high-performing as it is human. Among other perks, Reorg employees enjoy competitive health benefits, matched 401k and pension plans, Paid time off, generous parental leave, gym subsidies, educational reimbursements for career development, recognition programs, pet-friendly offices, and much more. 

The Role

At Reorg, we would like to make data scientists and AI engineers more productive, speed up our model development, enable ML technology to be applied in SOC2 production environments, ensure the responsible use of AI systems, and achieve and maintain the SOC2 compliance. The individual for this role will develop a vision and set the AI team's strategic direction, aligning with internal and external client needs, business growth, and overall company strategy.


  • Building and maintaining ML/NLP/LLM pipelines: Designing and implementing automated pipelines to train, evaluate, deploy, and monitor machine learning models, as well as creating and maintaining comprehensive documentation of these pipelines. This includes integrating with data sources, training environments, and production platforms. Integrating on the Engineering side with technologies such as Kafka and ensuring the model call requests happen smoothly.
  • ML/NLP/LLM infrastructure management: Provisioning and managing the infrastructure required for machine learning projects including servers, databases, storage, networking, security, etc. Optimizing resource allocation and costs.
  • Data management: Developing data infrastructure and processes to acquire, validate, transform, and feed data into ML/LLM models. Tracking data provenance and ensuring compliance with regulations.
  • Deployment automation: Packaging trained models and deploying them for integration into production systems. Managing model versions and updates with minimal disruption to services by using Dockerization.
  • Monitoring and optimization: Collaborating with data scientists and engineers to optimize ML models for performance, scalability, and efficiency. Identifying and implementing strategies to enhance speed, reduce latency, and optimize resource utilization. Developing metrics, logs, dashboards, and alerts to monitor the performance of ML models in production. Detecting data or model quality issues and performance degradation over time. Continuously improving ML pipelines and infrastructure.
  • Governance and SOC2 compliance: Implementing controls and processes to govern responsible development and use of ML models including compliance with data security, privacy, and other regulations. Promoting transparency and oversight of ML systems and promptly maintaining CI/CD pipeline and documentations.
  • Collaboration: Working closely with data scientists, engineers, and business stakeholders to enable the development and application of machine learning technology. Providing MLOps expertise and guidance to maximize productivity and impact.
  • Staying up to date with technology: Continuously learning and expanding their knowledge of machine learning, DevOps tools, cloud platforms, automation frameworks, and related technologies to provide innovative solutions and value. Keeping up with industry best practices.


  • 5+ years of experience in ML ops
  • Bachelor's Degree and AWS certifications
  • Master’s strongly preferred

At Reorg, we consider a range of factors in connection with compensation decisions, including experience, skills, location, and our business needs and limitations. As a result, compensation may vary within and across similar roles and positions. Please note that the salary range information below is a good faith estimate for this position and actual compensation for any individual may fall outside this range if warranted by the circumstances applicable to that individual. If we identify a role that would be suitable for a broader range of skills and experience such that we would consider hiring at multiple levels then the range listed below may reflect that breadth.

The salary range estimate for this position is $170,000-$200,000. 

The actual compensation will be at Reorg’s sole discretion and will be determined by the aforementioned and other relevant factors. This position is eligible for an annual discretionary bonus.

Reorg provides equal employment opportunities (EEO) to all employees and applicants for employment without regard to race, color, religion, sex, national origin, age, disability or genetics. In addition to federal law requirements, Reorg complies with applicable state and local laws governing nondiscrimination in employment in every location in which the company has facilities. This policy applies to all terms and conditions of employment, including recruiting, hiring, placement, promotion, termination, layoff, recall, transfer, leaves of absence, compensation, and training.

Join 27186+ Machine Learning Engineers, receiving daily job alerts.