Are you ready to take on a technical challenge and collaborate on an innovative product?
About the Role
We are building an unparalleled data collection pipeline, deployed on AWS, written in Go and Typescript.
The customer-facing part of the solution is a Typescript and GraphQL-based web application, also deployed on AWS using the serverless paradigm. Most of our code runs as AWS's Lambda functions, with Terraform managing our infrastructure.
We're seeking a skilled engineer to join us on a 12-month contract basis. In this role, you'll drive the strategy and execution of ML systems that power our product, from ideation to deployment. You'll collaborate across teams, mentor others, and ensure our ML efforts are scalable, impactful, and aligned with business goals.
Responsibilities:
* Define the ML strategy and lead the development of scalable, production-grade machine learning systems on AWS.
* Guide the design of robust pipelines to prepare and enrich structured and unstructured data for ML tasks.
* Develop and oversee models for tasks such as data classification, entity resolution, relationship detection, and summarization.
* Lead experimentation with LLMs (e.g., GPT, BERT, LLaMA), including prompt engineering, embedding generation, fine-tuning, and RAG approaches.
* Collaborate with product, engineering, and data teams to integrate ML into product features that solve real customer problems.
* Establish and maintain best practices for model evaluation, monitoring, observability, and reproducibility.
* Mentor engineers and help shape a high-performance, learning-oriented ML team.
* Stay ahead of trends in machine learning and AI, and identify opportunities to apply emerging techniques within our product.
Requirements:
* Bachelor's or Master's degree in Computer Science, Machine Learning, or a related field.
* 6+ years of hands-on experience in applied machine learning, with at least 1–2 years in a technical leadership or lead role.
* Strong Python skills and fluency with ML/NLP libraries and frameworks (e.g., Pandas, scikit-learn, Hugging Face, PyTorch, boto3).
* Proven experience deploying ML systems on AWS (e.g., SageMaker, Lambda, ECS).
* Experience working with semi-structured and unstructured data at scale (e.g., NoSQL, web data, nested JSON).
* Deep understanding of LLM-based workflows and practical deployment (prompt tuning, embeddings, vector search, etc.).
* Familiarity with MLOps practices and tools (e.g., CI/CD for ML, monitoring, versioning).
* Experience with infrastructure-as-code tools like Terraform is a plus.
* Excellent communication and collaboration skills. Business-level English required.
What We Offer
* A flat hierarchical structure with room for innovation in a highly motivated team.
* A start-up environment that is passionate about quality, problem-solving, and building beautiful software.