Software Engineer 2, Machine Learning
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Job Description
Machine learning engineer developing statistical, generative AI, and LLM-powered agent solutions. Deploying scalable ML systems and agent pipelines for an education-focused startup.
Responsibilities:
- Collaborate with product managers and business stakeholders to understand business challenges and translate them into machine learning problems and actionable project requirements.
- Research and design solutions using statistical modeling, machine learning, and generative AI/LLMs.
- Collaborate with machine learning engineers to develop and refine solutions.
- Work with Applied AI/ML engineers to build LLM-powered agentic systems for real product use cases.
- Design, develop, and optimize agent pipelines and APIs.
- Build tests for agent behaviors and contribute to evaluation frameworks.
- Deploy machine learning solutions into scalable production environments on cloud platforms such as AWS, Azure, and GCP.
- Ensure deployed solutions are effective.
- Stay informed about developments in AI/ML, publications, best practices, evaluation methodologies, technology stacks, and relevant tools.
Requirements:
- 2+ years of full-time experience as an ML engineer at tier-1 product-based companies.
- MIMP: An AI-first approach for solving problems.
- Hands-on experience building statistical and machine learning models, with proven expertise across traditional methods (regression, classification, clustering, tree-based models) and deep learning techniques (neural networks, CNNs, RNNs).
- Solid prompt engineering skills.
- Good debugging and online searching skills.
- Good communication skills.
- Good problem-solving skills, including ability to solve hard-level data structures and algorithms problems.
- Hands-on experience building and deploying AI Agents using LangChain, LangGraph, OpenAI API, Gemini API, Anthropic API, etc. (good to have).
- Hands-on experience with multi-step agent workflows, orchestration, and evaluating agent reliability (good to have).
- Hands-on experience with vector databases (Pinecone, Weaviate) and semantic search (good to have).
- Hands-on experience fine-tuning open-weight language models and deploying and maintaining them in scalable production environments on cloud platforms such as AWS, Azure, or GCP (good to have).
Benefits:
- Permanent remote / work-from-home culture
- Opportunity to work on a cutting-edge AI technology stack
- An AI-first approach to solving real-world problems
- Complete ownership and trust from leadership
- Your ideas and concerns are actively heard by the leadership team
- Freedom to experiment, fail, and learn
- Rapid career progression for high performers
- Work with a high-caliber team of engineers and industry experts
- Learn how a fast-growing startup builds and scales
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