About CompScience

At CompScience, we're not just building software, we're saving lives. We're a high-growth startup on a mission to prevent 1 million workplace injuries through bold technological innovations, ensuring that everyone can go home safe at the end of the day.

Founded in 2019 and backed by investors from SpaceX, Tesla, and Anduril, we've assembled a powerhouse team that bridges two worlds:

• Cutting-Edge Technology: Our product, design, and engineering teams are composed of distinguished computer vision engineers, software architects, data scientists and product and design leaders from Amazon R&D, Meta, and the self-driving car industry. They bring unparalleled expertise in AI, machine learning, and design to the realm of workplace safety.

• Insurance Acumen: Our insurance team is made up of seasoned professionals who understand the nuances of workers' compensation policies. They work hand-in-hand with our tech experts to translate advanced analytics into tangible insurance products that truly serve our clients' needs.

Our groundbreaking perception-based risk assessment program, the first of its kind, provides the most comprehensive data stream available for risk analysis and monitoring and has proven to significantly reduce accidents in some of the world's most hazardous occupations.

About the Role

We are looking for an experienced and self-motivated Sr MLOps Engineer to join our growing team and take ownership of the infrastructure that powers our core machine learning products. As a key member of our engineering organization at a fast-growing Series B startup, you will be responsible for designing, building, and maintaining the systems that automate the entire lifecycle of our ML models—from data pipelines and training to deployment and production monitoring. This is a high-impact role where you will collaborate closely with our data science and engineering teams to ensure our cutting-edge risk assessment and underwriting models are scalable, reliable, and continuously improving.

Responsibilities

• Design, build, and own the end-to-end MLOps infrastructure on AWS, with a heavy emphasis on scalable data engineering and reliable, cost-efficient ML systems.

• Implement and manage high-throughput, event-driven ML workflows (S3, Lambda, SQS, Step Functions, Batch) to support both data-centric pipelines and model execution.

• Develop and maintain robust CI/CD pipelines for model deployment and promotion, enforcing best practices for Git, semantic versioning, and multi-branch release strategies.

• Orchestrate complex data pipelines for the ingestion, processing, and updating of embeddings in vector databases (e.g., Qdrant, ChromaDB).

• Establish and manage systems for training phase management and experiment tracking (e.g., MLflow, SageMaker Experiments) and evaluate modern model serving tools (e.g., BentoML).

• Implement comprehensive security measures, including least-privilege access control (IAM) and secure credential management for models and APIs.

• Collaborate with data science teams to translate prototypes (including LLMs and standalone APIs) into production-grade services with clear monitoring strategies for production model health.

Required Experience

• Bachelor's degree in Computer Science, Engineering, or a related technical field.

• 5+ years of professional experience in MLOps, DevOps, or a senior Data Engineering role with a focus on operationalizing machine learning models.

• Expert-level proficiency in Python for pipeline automation and scripting, including extensive experience with the AWS SDK (Boto3) and Bash.

• Deep, hands-on experience with core AWS services, including S3, Lambda, SageMaker, IAM, and a solid understanding of networking within VPCs.

• Proven experience building and deploying containerized applications (Docker), especially for serving ML models and LLM-based APIs.

• Deep familiarity with Git workflows (branching, merging, rebasing) and experience implementing CI/CD pipelines using tools like GitHub Actions or AWS CodePipeline.

• Demonstrated experience in designing and orchestrating complex, data-engineering-heavy pipelines, from data ingestion through to production inference.

Nice-to-have

• An active AWS Certification, such as AWS Certified Machine Learning – Specialty or AWS Certified DevOps Engineer – Professional.

• Proven experience designing and implementing comprehensive monitoring strategies and observability dashboards (CloudWatch, Grafana) to track model drift, latency, and throughput.

• Familiarity with managing hybrid or edge inference deployments (Greengrass, Jetson) and supporting model fine-tuning workflows.

Working at CompScience

Compensation: CompScience is committed to fair and equitable compensation practices. The annual salary range for this role is $175,000 – $225,000. Compensation is determined within the range based on your qualifications and experience. Our total compensation package also includes equity and comprehensive benefits.

Benefits at CompScience:

• Fast-paced startup environment where your ideas can quickly become reality

• Opportunity to wear multiple hats and grow beyond your job description

• Remote-first culture with home office support

• Comprehensive health benefits (Medical, Dental, Vision, HSA)

• 401(k) plan and life insurance

• Flexible time off and 12 weeks parental leave

• Professional development reimbursement

Our Ideal Teammate:

• Thrives in a fast-paced startup and is comfortable navigating ambiguity

• Excited to wear multiple hats and grow rapidly

• Committed to our mission of saving lives through technology

Compensation Range: $175K - $225K