Software Engineer 2 , Machine Learning

Accountabilities: Collaborate with product managers and business stakeholders to understand business challenges and translate them into well-defined machine learning problems and actionable requirements. Research, design, and implement effective solutions using statistical modeling, machine learning, deep learning, and generative AI technologies. Develop and refine machine learning models using techniques such as regression, classification, clustering, tree-based models, neural networks, CNNs, and RNNs. Work closely with machine learning and applied AI engineers to develop solutions for real-world product use cases. Design, develop, and optimize LLM-powered agent pipelines, APIs, and multi-step agent workflows. Build and evaluate AI agent behaviors, contributing to testing strategies and evaluation frameworks that improve reliability. Apply prompt engineering techniques to improve the quality and effectiveness of AI-powered applications. Build semantic search and retrieval capabilities using vector databases and related technologies. Fine-tune open-weight language models and deploy them in scalable production environments. Take ownership of deploying machine learning solutions to cloud platforms such as AWS, Azure, or GCP and monitor their effectiveness in production. Investigate technical issues, debug models and applications, and implement improvements based on observed performance. Stay current with developments in AI and machine learning, including research publications, evaluation methodologies, best practices, technology stacks, and emerging tools. Contribute to technical discussions, share knowledge, and help continuously improve AI engineering practices. Requirements Strong hands-on experience building statistical and machine learning models using traditional approaches such as regression, classification, clustering, and tree-based methods. Experience with deep learning techniques, including neural networks, CNNs, and RNNs. Solid prompt engineering skills and practical experience applying generative AI technologies. Hands-on experience building and deploying AI agents using frameworks and APIs such as LangChain, LangGraph, OpenAI, Gemini, Anthropic, or comparable technologies. Experience designing multi-step agent workflows, orchestration patterns, and approaches for evaluating agent reliability. Practical experience with vector databases such as Pinecone or Weaviate and semantic search technologies. Experience fine-tuning open-weight language models and deploying and maintaining them in scalable cloud-based production environments. Hands-on experience with at least one major cloud platform, such as AWS, Azure, or GCP. Strong software engineering and debugging skills, with the ability to investigate complex technical problems independently. Strong problem-solving abilities, including proficiency with challenging data structures and algorithms problems. Ability to translate ambiguous business challenges into clearly defined machine learning problems and practical technical solutions. Strong communication and collaboration skills, with the ability to work effectively with product managers, business stakeholders, and engineering teams. Curiosity and commitment to staying current with rapidly evolving AI and machine learning technologies. Ability to work independently, take ownership, and operate effectively in a fast-moving environment. Benefits Permanent remote and work-from-home culture. Opportunity to work with a cutting-edge AI and machine learning technology stack. AI-first approach to solving practical, real-world business and product challenges. High degree of ownership and trust from leadership. Opportunity to contribute ideas and raise concerns directly with leadership. Freedom to experiment, learn from failures, and continuously improve. Rapid career progression opportunities for strong performers. Opportunity to work alongside experienced engineers and industry experts. Exposure to machine learning, generative AI, LLMs, AI agents, semantic search, and cloud technologies. Opportunity to learn how a fast-growing technology organization builds and scales AI-powered products.