Staff Machine Learning Engineer

Posted about 1 month agoViewed

💎 Seniority level: Staff

📍 Location: Brazil

🔍 Industry: Software Development

🏢 Company: VTEX👥 1001-5000💰 over 3 years ago🫂 Last layoff almost 3 years agoE-Commerce SaaS Information Technology Software

🗣️ Languages: English

🪄 Skills: AWSBackend DevelopmentDockerPythonSQLApache AirflowData AnalysisFrontend DevelopmentKafkaKubernetesMachine LearningMLFlowAPI testingData engineeringData scienceCI/CDRESTful APIsSoftware Engineering

Requirements:

Experience in building end-to-end ML pipelines: training, evaluation, optimization, deployment and monitoring, working closely with Data Scientists
Proficient in writing production-level code using best practices in software engineering to develop low-latency and high-throughput APIs
Skilled at integrating ML models with larger software systems, considering sync and async architectures and CI/CD aspects necessary to scale models in production

Responsibilities:

Design and implement production-level ML/AI systems that scale business value.
Create robust, maintainable pipelines.
Enhance performance.
Ensure AI/ML solutions are seamlessly integrated into VTEX's products.

Apply

Related Jobs

Apply

🔥 Senior Staff Machine Learning Engineer

Posted 29 days ago

📍 United States, Australia, Canada, South America

🧭 Full-Time

💸 188000.0 - 225000.0 USD per year

🔍 FinTech

🏢 Company: Flex

🔧 Requirements

6+ years of experience as a Machine Learning Engineer, with expertise in building and deploying machine learning models in production environments.
Strong proficiency in Python, or similar programming languages, and experience with ML libraries like TensorFlow, PyTorch, and scikit-learn.
Extensive experience with cloud platforms (e.g., AWS, GCP, Azure) and distributed computing frameworks (e.g., Spark, Kubernetes).
Proven track record of implementing end-to-end machine learning pipelines, from data preprocessing to production deployment and monitoring.
Strong background in model optimization, version control, and CI/CD practices for machine learning.
Excellent problem-solving abilities and the capacity to collaborate with cross-functional teams to deliver high-quality, production-ready systems.

💡 Responsibilities

Own the end-to-end lifecycle of machine learning projects, from data collection and preprocessing to model deployment, monitoring, and maintenance in a production environment.
Build, maintain, and optimize robust data pipelines that support model development, training, and deployment at scale.
Implement machine learning algorithms and models that meet performance, scalability, and reliability requirements in a production setting.
Collaborate with data scientists, engineers, and product teams to design and deploy machine learning systems that address business and product needs.
Continuously monitor and improve model performance, conducting experiments, tuning hyperparameters, and ensuring models meet business objectives.
Leverage distributed computing frameworks and cloud-based platforms to process large-scale datasets efficiently.
Stay up-to-date with the latest advancements in machine learning, software engineering practices, and deployment strategies to keep our systems cutting-edge.

AWSPythonCloud ComputingGCPKubernetesMachine LearningPyTorchAlgorithmsAzureData engineeringREST APISparkTensorflowCI/CDSoftware Engineering

Posted 29 days ago

Apply

🔥 Staff Machine Learning Engineer

Posted 2 months ago

📍 Canada, Latin America, Alabama, Arizona, California, Colorado, Connecticut, Florida, Georgia, Illinois, Indiana, Massachusetts, Minnesota, Nevada, New Jersey, New York, North Carolina, Oregon, Pennsylvania, Rhode Island, Tennessee, Texas, Utah, Virginia, Washington

💸 140000.0 - 220000.0 USD per year

🔍 Retail AI

🏢 Company: Lily AI

🔧 Requirements

10+ years in building large-scale machine learning solutions and ML Ops practices.
Working with LLM APIs and serving LLMs in-house at scale.
Proficiency in Kubernetes, RDBMS, and API-driven development.
Experience in model serving in low-latency, high-throughput use cases.
Knowledge of observability, data pipeline design, service scaling, and cost optimization.
Strong emphasis on code hygiene, including review, documentation, testing, and CI/CD practices.
Proficiency in Python and PyTorch.
Extensive experience with the scientific Python ecosystem.
Proficiency in cloud-native application development.
Action-oriented with the ability to articulate complex concepts.

💡 Responsibilities

Define, design, and maintain scalable Machine Learning data pipelines, training infrastructure, and inference systems.
Optimize, benchmark, and productionize deep learning models to extract high-value product attributes.
Drive cost efficiency and throughput improvements, owning relevant KPIs.
Promote and implement software engineering best practices across the team.
Shape and evolve the technical stack to meet business and technical needs.
Transition research prototypes into robust, production-ready systems.
Deploy, monitor, and continuously improve models in production environments.
Optimize model performance, focusing on memory usage and latency.
Automate workflows by building efficient pipelines and orchestration frameworks.
Develop tools and shared libraries to boost team productivity.

PythonKubeflowKubernetesMachine LearningMLFlowPyTorchAzure

Posted 2 months ago

Apply