Staff AI Software Engineer
About The Position
We are seeking a passionate Staff AI Software Engineer to own the full lifecycle of AI systems, from training and fine-tuning models to deploying and scaling them in production. This role operates at the intersection of machine learning, data science, and infrastructure engineering. You will join the Applied AI team, collaborating with engineers, data scientists, and product stakeholders to build AI capabilities for iManage’s enterprise work platform. This includes Ask iManage, our generative AI document assistant, as well as advanced document classification, extraction, and NLP-driven features. You will tackle complex problems in applying large language models and NLP at scale to enterprise documents and workflows. The ideal candidate is a self-starter who identifies problems proactively, coordinates across teams, and drives solutions to completion, with a strong consideration for cost, performance, and reliability at scale, and communicates with clarity and accountability.
Requirements
- A Bachelor's, Master's or Ph.D. in Computer Science, Machine Learning, Data Science, Artificial Intelligence, Statistics, or a related field
- The ability to work collaboratively across teams, communicate with precision, and take ownership from prototype to production
- 4+ years of experience in ML/AI engineering or software engineering with 3+ year of experience building and shipping in NLP and LLM systems into productions
- Deep proficiency in Python and modern AI frameworks (i.e., PyTorch and Hugging Face)
- Deep understanding of ML fundamentals including hands-on experience with both traditional ML/NLP and modern genAI/transformer architectures
- Demonstrated hands-on experience in fine-tuning language models and deploying them to production
- Experience with GPU optimization for both training and inference
- Experience with k8s deployment on cloud infrastructure (Azure/AWS/GCP) to optimize systems for scalability, latency, performance, operational complexity, and cost efficiency
Nice To Haves
- Experience in knowledge graph and multimodal LLMs
- Experience with distributed training (i.e, Pytorch distributed, Ray) and inference optimization framework (i.e, vLLM, SGLang)
- Experience with agentic engineering (agent harness, agent memory) and orchestration framework like LangChain/LlamaIndex, or similar tools
Responsibilities
- Owning the end-to-end ML lifecycle for AI systems from prototype to production, from model development and evaluation to scalable application design and production serving for NLP, genAI document intelligence, and agentic system use cases
- Collaborating with product and business stakeholders to translate requirements into viable technical solutions
- Deploying and optimizing ML/AI systems on GPUs and Kubernetes-based cloud infrastructure, including AKS or equivalent platforms, while balancing trade-offs across scalability, latency, performance, operational complexity, and cost efficiency.
- Applying modern engineering practices for production AI systems, including monitoring, observability, integration testing, containerized services, CI/CD pipelines, model/version tracking, and release governance.
- Driving architectural decisions for AI systems and mentoring team members to foster a culture of innovation and knowledge sharing
Benefits
- Flexible working policy
- Flexible work hours
- Internal development framework
- Access to unlimited courses in LinkedIn Learning
- Market competitive salary
- Annual performance-based bonus
- Comprehensive Health/Vision/Dental/Life Insurance
- 401k Retirement Savings Plan with a company match up to 4%
- HealthJoy, a healthcare concierge service
- Enhanced leave for expecting parents (20 weeks 100% paid for primary leave, and 10 weeks 100% paid for secondary leave)
- Flexible time off policy
- Company wellness days
- Free access to the Healthy Minds app
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What This Job Offers
Job Type
Full-time
Career Level
Senior
