Staff Software Engineer, Applied AI
New
V
ValenceEnterprise AI Coaching
If candidates are based in NYC or Toronto they can work hybrid in our offices, otherwise this role can be remote., Candidates must be comfortable working with colleagues in different time zones (UK).Full-TimeStaff
SalaryCompetitive salary including base + bonuses
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Job Details
- Experience
- 8+ years of experience in software engineering, AI/ML, data-intensive systems, AI/ML development
- Required Skills
- DockerPythonSQLMachine LearningNumpyPandasscikit-learnNLPLLMPySpark
Requirements
- 8+ years of experience in software engineering, AI/ML, data-intensive systems, or AI/ML development.
- Master's or Ph.D. in Computer Science, ML, Data Science, or a related field preferred.
- Familiarity with language systems such as NLP, conversational interfaces, and information retrieval.
- Comfort reasoning about model behavior, context, and evaluation.
- Experience with data science tools including NumPy, scikit-learn, Pandas, PySpark, SQL, and visualization tools.
- Comfort developing and deploying services in cloud environments like AWS, GCP, or Azure.
- Proficiency with containerization and orchestration tools like Docker and Kubernetes.
- Strong software engineering skills including writing maintainable code and debugging distributed systems.
- Ability to explain technical ideas clearly and work with cross-functional stakeholders.
- Eagerness to learn new technologies and contribute to team culture.
Responsibilities
- Architect and build enterprise-grade AI and conversational systems that power coaching workflows and user experiences.
- Develop, evaluate, and refine LLM-based components, balancing performance, scalability, and reliability.
- Integrate and manage diverse sources of structured and unstructured data to improve contextual understanding and output quality.
- Partner closely with product, engineering, and design to translate user needs into impactful technical solutions.
- Rapidly prototype and iterate on systems that span backend services, data pipelines, and frontend interactions.
- Build tooling, tests, and automation to support reliable model deployment, observability, and continuous improvement.
- Streamline data and science workflows to enable fast experimentation and data-driven decisions.
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