Unknown company
Machine Learning Engineer
- Design, build, and deploy machine learning models and AI systems in production environments
- Develop components such as:
- Model inference services
- Data and feature pipelines
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Complex recommendation and matching services
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Vision based analysis systems
- Evaluation and monitoring pipelines
- Optimize models for performance, reliability, and cost efficiency
- Contribute to the development of AI agents and multi-step workflow automation systems
- Build systems that integrate with enterprise tools and APIs
- Implement tool-use frameworks, memory mechanisms, and evaluation loops
- Experiment with LLMs, foundation models, and fine-tuning approaches
- Help translate AI research advances into practical, scalable solutions
- Write high-quality, maintainable, and well-tested code
- Participate in architecture design and technical reviews
- Contribute to CI/CD pipelines and MLOps workflows
- Implement observability and monitoring for AI systems in production
- Follow security, compliance, and responsible AI best practices
- Partner with product, data engineering, and infrastructure teams
- Help identify high-impact AI use cases within portfolio companies
- Support integration of shared AI components into business applications
- Communicate technical tradeoffs clearly to both technical and non-technical stakeholders
- 3+ years of experience in software engineering, data science, or machine learning (more for senior roles)
- Experience building and deploying production software systems
- Strong programming skills in Python (experience in additional languages is a plus)
- Familiarity with ML frameworks (e.g., PyTorch, TensorFlow, scikit-learn)
- Understanding of modern AI architectures, including LLM-based systems
- Experience working with cloud environments (AWS, Azure, or GCP)
- Strong problem-solving skills and attention to detail
- Experience with:
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Fine tuning, experimentation, etc.
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Rapid development using AI tools
- Agent frameworks and orchestration tools
- Distributed systems or microservices architecture
- Model monitoring and evaluation frameworks
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- Experience building reusable libraries or shared infrastructure
- Exposure to SaaS products or enterprise software environments
- Background in optimizing models for performance and cost
- Intermediate ML Engineer – Contributes independently to projects, builds production features, collaborates cross-functionally.
- Senior ML Engineer – Owns complex systems end-to-end, drives architectural decisions, mentors others.
- Principal / Staff ML Engineer – Defines technical direction, leads cross-portfolio initiatives, designs shared frameworks and scalable AI infrastructure.
- Strong engineering fundamentals
- Practical, impact-driven AI development
- Curiosity and willingness to experiment responsibly
- Ownership mindset and bias toward execution
- Ability to balance innovation with reliability
- Work on high-impact AI systems across a diverse portfolio of leading software businesses
- Build reusable infrastructure that scales across industries
- Collaborate with experienced engineering and executive leadership
- Shape the next generation of intelligent enterprise software