[Hiring] ML Platform Engineer @Bask Health

Role Description

We're looking for an ML Platform Engineer, Evaluation Systems to build and maintain the infrastructure that allows Bask Health to evaluate machine learning models and AI-powered applications reliably, consistently, and at scale.

• Develop the platform services, data pipelines, execution environments, and monitoring capabilities that support machine learning evaluation across engineering teams.
• This is a hands-on engineering role focused on the operational infrastructure behind evaluation systems.
• Work with cloud services, distributed processing, model integrations, databases, and automated workflows to support repeatable experiments and large-scale evaluation jobs.
• Help develop evaluation job orchestration, manage benchmark datasets, develop experiment tracking systems, support model comparisons, and create infrastructure that stores and analyzes evaluation results.
• Collaborate with machine learning engineers, applied scientists, AI evaluation engineers, and backend engineers to understand their technical requirements and build reusable systems that support different model types and evaluation methodologies.
• Improve the reliability and reproducibility of evaluation workflows, ensuring experiments can be repeated, datasets and configurations are versioned, failures are observable, and results can be compared accurately over time.
• Ensure evaluation infrastructure supports appropriate privacy controls, secure data access, and responsible handling of sensitive information.
• Contribute to architecture, build scalable systems, improve operational performance, and help engineering teams evaluate AI capabilities more effectively.

Qualifications

• 3+ years of professional experience in software engineering, machine learning engineering, platform engineering, or a closely related technical field.
• Experience building and maintaining production backend services or infrastructure.
• Strong programming skills in Python, TypeScript, or comparable languages.
• Familiarity with machine learning development and evaluation workflows.
• Experience developing APIs and backend service integrations.
• Understanding of distributed systems and asynchronous processing.
• Experience working with cloud-based infrastructure and production deployments.
• Familiarity with automated testing and continuous integration practices.
• Understanding of machine learning model versioning and experiment reproducibility.
• Experience working with relational databases and structured datasets.
• Familiarity with data pipelines and batch processing workflows.
• Experience designing reliable background job systems.
• Understanding of infrastructure scalability, performance, and fault tolerance.
• Familiarity with logging, monitoring, and production observability.
• Experience implementing retries, timeouts, and error recovery.
• Understanding of authentication, authorization, and secure service communication.
• Familiarity with configuration management and deployment automation.
• Experience writing maintainable, well-tested production code.
• Ability to troubleshoot complex backend or infrastructure issues.
• Understanding of software release processes and operational reliability.
• Experience using Git and collaborative engineering workflows.
• Ability to translate machine learning requirements into scalable infrastructure.
• Strong analytical thinking and problem-solving skills.
• Ability to collaborate effectively with machine learning engineers, applied scientists, and software engineers.
• Ability to independently own engineering projects from design through deployment.

Requirements

• Design, build, and maintain machine learning evaluation infrastructure across Bask Health.
• Develop platform services that execute and manage automated model evaluation workloads.
• Build scalable systems for running large batches of model and AI application evaluations.
• Implement job orchestration, scheduling, and execution management for evaluation pipelines.
• Develop infrastructure for storing and versioning evaluation datasets.
• Build systems for tracking model configurations, prompts, parameters, and evaluation results.
• Implement reproducible experiment workflows that support consistent model comparisons.
• Develop automated pipelines for benchmarking machine learning models.
• Build APIs and services that allow engineering teams to submit and monitor evaluation jobs.
• Create infrastructure for comparing evaluation results across model versions and experiments.
• Support evaluation workflows for large language models, predictive models, and retrieval systems.
• Develop asynchronous processing systems for computationally intensive evaluation tasks.
• Implement monitoring for evaluation job performance, failures, latency, and resource usage.
• Build logging and tracing capabilities that help engineers investigate evaluation problems.
• Develop systems for managing evaluation artifacts, reports, and historical results.
• Integrate evaluation infrastructure with model registries and ML development workflows.
• Implement automated quality checks that support controlled model releases.
• Build reliable retry, timeout, and failure recovery mechanisms for evaluation workloads.
• Optimize evaluation execution performance, infrastructure utilization, and operational costs.
• Support distributed evaluation workloads across cloud-based compute resources.
• Collaborate with machine learning engineers to define evaluation infrastructure requirements.
• Work with applied scientists to support reproducible experiments and benchmark comparisons.
• Develop tools that simplify evaluation configuration and execution for engineering teams.
• Implement secure access controls for evaluation datasets and results.
• Ensure evaluation systems respect tenant boundaries and approved data-handling practices.
• Investigate infrastructure incidents and improve evaluation platform reliability.
• Document evaluation infrastructure, deployment procedures, and operational standards.
• Own assigned ML evaluation platform projects from technical design through deployment, monitoring, and continuous improvement.

Company Description

Bask Health is a software platform that enables entrepreneurs, doctors, and healthcare businesses to launch, manage, and grow direct-to-consumer telehealth businesses.

• Our platform brings together patient experiences, clinical workflows, pharmacy operations, and commerce tools in one place.
• We're an entrepreneurial, product-focused engineering team that values ownership, technical excellence, and building software that solves real problems.
• Engineering at Bask is AI-first and builder-led.