Hop Labs · 18 hours ago
Software Engineer (ML)
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Artificial Intelligence (AI)Information Technology
No H1B
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Responsibilities
You have experience building and improving ML-powered systems and an interest in developing your skills there – bonus points if you’re interested in LLMs, generative AI, or deep learning in general.
You enjoy solving hard technical problems at scale, for real business impact.
You’re comfortable proposing end-to-end technical architectures that balance modularity, scalability, operations, security, and cost.
You understand that clarity and simplicity -- in code and in communication -- are worth striving for.
You have 5+ years of professional experience in software engineering and are able to present as an expert in client-facing situations.
Experience with construction and design of the entire ML pipeline, including training, deployment, and hosting in a production environment, using engineering best practices (e.g., unit testing, monitoring, logging).
Fluency in the Python ML/data science stack (e.g., PyTorch, Scikit-Learn, Pandas).
Proficiency in cloud engineering (AWS/Azure/GCP) and infrastructure-as-code (e.g., Terraform or equivalent).
Proficiency with ETL/data pipelines and data stores.
Facility with web application technologies (e.g., FastAPI, UI development).
Open to all backgrounds, though deep learning/LLM background strongly preferred.
Qualification
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Required
Experience building and improving ML-powered systems
Interest in developing skills in ML, LLMs, generative AI, or deep learning
Enjoy solving hard technical problems at scale for real business impact
Comfortable proposing end-to-end technical architectures that balance modularity, scalability, operations, security, and cost
Understanding that clarity and simplicity in code and communication are worth striving for
5+ years of professional experience in software engineering
Ability to present as an expert in client-facing situations
Strong sense of ownership and initiative
Clear and consistent communication
Collaborative mindset
Clear thinking and attention to detail for complex projects
Comfort with operating independently as well as part of a small, targeted team
Experience with construction and design of the entire ML pipeline, including training, deployment, and hosting in a production environment, using engineering best practices (e.g., unit testing, monitoring, logging)
Fluency in the Python ML/data science stack (e.g., PyTorch, Scikit-Learn, Pandas)
Proficiency in cloud engineering (AWS/Azure/GCP) and infrastructure-as-code (e.g., Terraform or equivalent)
Proficiency with ETL/data pipelines and data stores
Facility with web application technologies (e.g., FastAPI, UI development)
Residency and authorization to work in the U.S. or Canada
Preferred
Deep learning/LLM background strongly preferred