What are the responsibilities and job description for the Machine Learning Engineer (Associate) position at Jobright.ai?
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Job Summary:
Shipium is on a mission to solve the ‘Prime problem’ that modern retailers face today regarding fast and on-time delivery. The Machine Learning Engineer will be responsible for designing, deploying, and optimizing scalable machine learning platforms, while also leading initiatives in Generative AI and collaborating with data scientists to enhance the company's AI solutions.
Responsibilities:
• Architect and implement a scalable, high-performance machine learning platform to support model development, deployment, monitoring, and analysis for both predictive and Generative AI models.
• Lead the technical strategy and evaluation for our Generative AI infrastructure. This includes assessing the trade-offs between managed services and self-hosted open-source models, defining our LLM hosting strategy, and validating the end-to-end architectural approach for scalable, reliable AI features.
• Ensure the platform supports a wide range of ML use cases, including real-time prediction serving, batch processing, and model experimentation.
• Design and implement robust LLM orchestration for advanced applications, enabling the integration of our proprietary predictive models with LLMs to power new insights and workflows.
• Ensure the platform supports a wide range of ML use cases, including real-time prediction serving, batch processing, model experimentation, and advanced Generative AI applications.
• Optimize system performance and model latency to ensure robust, low‑latency inference across distributed systems, with a specific focus on the unique challenges of LLM serving.
• Identify bottlenecks, evaluate, and integrate new technologies and tools.
• Collaborate closely with data scientists to productionize both traditional predictive models and novel Generative AI solutions, focusing on systems that combine proprietary model outputs with LLMs to create actionable insights.
• Contribute to the overall quality of the codebase, ensuring maintainability and best practices.
• Drive ML / DS best practices, give technical recommendations on challenging problems.
Qualifications:
Required:
• Proficiency in Python and deep experience with its data science and ML ecosystem (e.g., PyTorch, TensorFlow, scikit-learn, Pandas, NumPy).
• Hands-on experience with Generative AI frameworks and libraries such as LangChain, LlamaIndex, or Hugging Face Transformers.
• Expertise in building and maintaining MLOps infrastructure, including containerization (Docker), orchestration (Kubernetes), and CI/CD pipelines for both traditional ML models and LLM-based applications.
• Proven skill in managing cloud resources using Infrastructure as Code (Terraform).
• Extensive hands-on experience with cloud platforms, particularly AWS. Required experience with core services (S3, EC2, Lambda) and ML services (SageMaker).
• Direct experience with or deep knowledge of managed Generative AI services like AWS Bedrock, Amazon Titan, or equivalents (e.g., Google Vertex AI, Azure OpenAI Service).
• Advanced proficiency in SQL for complex data extraction and transformation.
• Experience with a variety of data storage solutions, including relational databases, NoSQL databases, and vector databases (e.g., Pinecone, Weaviate, ChromaDB).
• Master’s Degree in Computer Science, Software Engineering, or a related field and 4-5 years of experience building and managing production-level machine learning platforms and infrastructure, with a focus on model deployment, optimization, and scalability; demonstrated ability to improve the performance, reliability, and cost-efficiency of ML systems; strong experience with cloud-based ML infrastructure (AWS, GCP, Azure) and MLOps practices; and/or equivalent combination of education and experience such as a PhD in Computer Science, Data Science, Mathematics, Statistics, or related quantitative field and strong knowledge of machine learning.
Company:
Shipium is the Premier Shipping Platform for retail and ecommerce. We make fast shipping the cornerstone of a great shopping experience. Founded in 2019, the company is headquartered in Seattle, Washington, USA, with a team of 51-200 employees. The company is currently Growth Stage.
