ML Platform Engineer

Posted 4 months agoInactiveViewed

💎 Seniority level: Middle, 1–3 years

📍 Location: USA

💸 Salary: 100600.0 - 148000.0 USD per year

🔍 Industry: Fashion and technology

🏢 Company: Stitch Fix👥 5001-10000💰 $11,850,773 over 7 years ago🫂 Last layoff 12 months agoE-Commerce Retail Fashion Apparel

⏳ Experience: 1–3 years

🪄 Skills: AWSPostgreSQLPythonJavaKafkaRedisSpark

1–3 years of experience in software development, specifically in data and ML infrastructure.
Proven track record of building scalable, distributed production systems.
Exceptional coding and design skills, primarily using Python and Java.
Proficient in big data technologies such as Kafka, Spark, Hive/Iceberg, Postgres, and Redis.
Hands-on experience with AWS or other cloud providers.
Strong cross-functional communication skills.

Build and maintain the critical infrastructure for machine learning.
Design, develop, and support scalable services and frameworks for ML model training and deployment.
Contribute to day-to-day operations of the ML Platform team and drive improvements.
Collaborate with data scientists to unlock the full potential of the platform.

Posted about 1 month ago

📍 United States

🧭 Full-Time

💸 172000.0 - 207900.0 USD per year

🔍 Software Development

🔧 Requirements

Extensive hands-on experience in ML infrastructure, including building pipelines and frameworks from scratch.
Expertise in anomaly detection, fraud detection, predictive analytics, and confidence scoring.
Strong Python skills, with proficiency in PyTorch, TensorFlow, and Spark (PySpark preferred, not Java-based Spark).
Experience working with data warehouses (Snowflake), streaming systems (Kafka), and large-scale ML systems.
A track record of delivering production-ready ML systems—not just research or Jupyter notebooks.
Ability to thrive in a fast-paced, fully remote, distributed team environment.

💡 Responsibilities

Design and build the AI/ML infrastructure to power cost anomaly detection and predictive analytics.
Lay the foundation for ML-driven cost optimizations—detecting issues and automating remediation within dbt.
Work hands-on with Snowflake, Kafka, PyTorch, TensorFlow, Ibis, and Pandas to process billions of data points.
Collaborate cross-functionally with Product, Engineering, and Design to integrate ML models into dbt workflows.
Provide technical leadership in defining the AI/ML strategy for cost optimization and efficiency.
Influence the growth of our AI/ML engineering team as we scale.

PythonSQLKafkaMachine LearningPyTorchSnowflakeData engineeringData sciencePandasSparkTensorflowData visualizationData modeling

Posted about 1 month ago

Posted about 1 month ago

📍 United States, Canada

🧭 Full-Time

🔍 Software Development

🔧 Requirements

Write high-quality code. We mostly work in Python.
Experience across the entire spectrum of data engineering, including: Data stores (e.g., ClickHouse, ElasticSearch, Postgres, Redis, and Neo4j), Data pipeline and workflow orchestration tools (e.g., Airflow, DBT, Luigi, Azkaban, Storm), Data processing technologies and streaming workflows (e.g., Spark, Kafka, Flink), Deployment and monitoring infrastructure in public cloud platforms (e.g., Docker, Terraform, Kubernetes, Datadog)
Loading, querying and transforming large data sets
AI and ML Integration tools like MLflow, Feast, ONNX, Apache Beam
You're comfortable working with noisy, dirty, and unstructured data to cleanse, scrape and convert it into structured data

💡 Responsibilities

Build and enhance AI/ML Model Performance
Ensure data accuracy, consistency, and reliability, leading to better model training and inferencing
Build low latency high throughput pipelines to enable real-time AI applications
Enable AI-powered insights for quick decision-making
Integrate and evaluate new AI technologies, accelerating innovation
Help optimize operational costs while adding value
Collaborate with data science, engineering, and business teams to shift focus on model building instead of data wrangling by embedding AI models into applications and platforms

DockerPythonElasticSearchKafkaKubernetesMLFlowAirflowClickhouseData engineeringPostgresSpark

Posted about 1 month ago