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Worldly · 1 month ago

ML Ops Engineer - Data Lake & AI Infrastructure

United States

Full-time

Remote

Mid, Senior Level

$145K/yr - $185K/yr

4+ years exp

Worldly is the world’s most comprehensive impact intelligence platform, providing real data to businesses on impacts within their supply chain. The ML Ops Engineer will design, deploy, and support the data infrastructure and AI systems that unify structured and unstructured data at scale, helping to unlock insights and power compliance with evolving regulations.

ApparelConsultingConsumer GoodsData ManagementInformation TechnologyReal TimeService IndustrySoftwareSupply Chain ManagementSustainability

Responsibilities

Design and deploy data lakehouse infrastructure using open-source technologies (e.g., MinIO, Apache Iceberg, Trino) to ingest and manage high-volume structured and unstructured data

Build and scale ML pipelines using modern tools such as MLflow, LangChain/Haystack, and orchestrate them via Airflow or Dagster

Implement data ingestion and transformation workflows using tools like Apache NiFi, Airbyte, and dbt

Support federated querying and real-time analytics via Trino, ClickHouse, or StarRocks

Enable retrieval-augmented generation (RAG) and other LLM-powered applications by integrating the data lake with AI/ML systems

Develop CI/CD pipelines for ML models, infrastructure-as-code, and data pipeline deployments

Monitor, debug, and optimize data and ML services running across distributed environments (including mainland China)

Collaborate cross-functionally with data scientists, platform & DevOps engineers, and sustainability analysts to translate real-world use cases into scalable MLOps workflows

Qualification

Data lakehouse infrastructureML pipelinesData ingestion workflowsCI/CD pipelinesMonitoring ML performanceContainerized data toolsData engineering best practicesKubernetesSoft skills

Required

4+ years of experience in ML engineering, MLOps, or data infrastructure roles

Proven hands-on experience with containerized open-source data tools such as: Object stores: MinIO, Ceph, or HDFS; Table formats: Apache Iceberg, Hudi, or Delta Lake; Query engines: Trino/Presto, ClickHouse, or DuckDB; Workflow orchestration: Airflow, Dagster, or Prefect; ML tools: MLflow, LangChain, Hugging Face, or vLLM; ETL/ELT tools: Airbyte, NiFi, dbt

Experience managing infrastructure across multiple regions, including self-hosted deployments (Kubernetes, Docker Compose, Terraform, etc.)

Experience monitoring ML prediction performance, drift metrics, and pipeline tools

Strong understanding of data engineering best practices, including security, governance, and versioning

Preferred

Experience deploying AI/ML infrastructure in China-compatible cloud environments (e.g., Alibaba Cloud, Huawei Cloud)

Familiarity with retrieval-augmented generation (RAG) pipelines and unstructured document indexing

Exposure to sustainability or supply chain data models

Contributions to open-source data or MLOps projects

Experience supporting data quality pipelines and/or data privacy frameworks (GDPR, CSRD, etc.)

Benefits

Comprehensive benefits offerings. 90% employee premium and 75% spouse/dependent premium covered by Worldly.

Company-sponsored 401k with up to 4% match.

Incentive Stock Options

100% Parental Paid Leave

Unlimited PTO

13 company holidays

Earn a competitive salary and performance-based bonuses. Get healthcare, retirement matching, and equity for US employees.

Use the office stipend to get the supplies you need. Combat Zoom fatigue with Flex Fridays.

Flexible time off. Take the time you need to recharge. Our culture encourages team members to explore and rest to be their best selves.

We're remote, not lonely. Join the culture committee, coffee chats, or a variety of other interest groups.

Company

Worldly

Worldly is the leading sustainability data and analytics platform for the consumer goods industry, empowering brands, retailers, and manufacturers to turn primary data into strategic action.

Founded in 2019

San Francisco, California, USA

51-200 employees