AI / ML Engineer
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Role details
Tech stack
Job description
You build the core of the product. Real-time voice analysis, in-call coaching triggers, post-call scoring. You will work directly with the founding team on the models that power every eNZeTi session. This is not a research position. Every model you ship runs in production during live law firm intake calls, coaching coordinators in real time. Sub-200ms latency is not a stretch goal. It is a requirement., * Train and fine-tune transformer models on legal intake conversation data
- Engineer sub-200ms inference pipelines for in-ear real-time coaching delivery
- Build post-call scoring models that grade coordinator performance across 10+ dimensions
- Own model evaluation, quality metrics, and continuous improvement loops
- Collaborate with backend engineering on streaming audio architecture
- Define the long-term ML architecture as data volume and customer base scale
- Mentor future ML team hires as the engineering org grows, * Real call data from active law firm customers - labeled, structured, growing daily
- A founding team that understands AI deeply and will not slow you down
- Greenfield ML infrastructure - no legacy debt, your architectural decisions stick
- A clear roadmap from coaching prompts to predictive case outcome scoring
Why This Role Matters
94% of law firm intake calls go completely unreviewed. Coordinators get no feedback, no coaching, no real-time support on calls worth $5,000 to $50,000 in case value. What you build changes that. Every model improvement is a law firm that closes more cases, a coordinator who gets better at their job, and a client who actually gets the legal help they called about.
Requirements
- 3+ years shipping production ML systems - not notebooks, not prototypes
- Real-time audio, speech, or conversational AI experience (non-negotiable)
- Python, PyTorch or TensorFlow, AWS or equivalent cloud infrastructure
- Strong understanding of intent classification, NER, and dialogue systems
- Experience optimizing model inference for latency-sensitive production environments
- Ability to work directly with founders and ship without hand-holding
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