Note: The job is a remote job and is open to candidates in USA. Altera AI is building an AI litigation associate that produces verified, court-fileable legal work for law firms. The Staff AI Engineer will own the company’s AI systems end to end, including agentic workflows, deterministic drafting pipelines, retrieval and grounding, model and prompt strategy, evaluation systems, quality gates, safety scoring, privacy measurement, and production readiness across jurisdictions.
Responsibilities
- The agentic lane. The agent loop and a deliberately constrained tool surface — read the record, search case law, verify citations, write sections, validate, request attorney input — plus the policy gates that make our guarantees non-negotiable: verification is mandatory, citations must verify, an attorney confirms before anything is finishable, and the privacy boundary holds. The agent chooses the path. It doesn't choose the guarantees
- The drafting pipeline. The LLM-facing stages of the deterministic lane — extraction, legal theory, strategy, document planning, drafting, verification — and the validator suite that catches fabricated exhibits, phantom authority, and unverified citations before an attorney ever sees them
- Retrieval and grounding. Case-law search over a large public corpus and grounding against the matter record. Whether the right authority surfaces, and whether the draft is actually anchored in the file rather than in the model's priors
- Model and prompt strategy. Migrations across model generations, routing between model tiers, caching and token economics, and the experiment discipline to know whether a change actually helped
- The quality gate. A blocking gate rather than an advisory one: thresholds calibrated against measured noise, meaningful baselines, a no-regression guard, and evidence strong enough to stop a release
- The safety scorers. Faithfulness — does every factual claim in a draft trace back to the record? — and citation integrity: fictitious, mismatched, misattributed holding, conflated authority, wrong statute
- The evaluation corpus. Build the versioned ground-truth datasets behind our quality claims — real legal tasks, attorney adjudication, provenance, disagreement handling, jurisdiction coverage, and hard cases accumulated from production
- Production evaluation. Measurement over appropriately controlled real production drafts, not just an offline fixture set, with drift detection as the jurisdiction count grows
- Proving new jurisdictions. As we expand nationally, a new forum ships only when there's evidence its output is filing-grade. Expansion that outruns measurement just means shipping wrong documents faster — you own the gate that prevents it
- Privacy as a measured system. De-identification scored on false negatives — leaked PII — and false positives, where over-redaction quietly degrades the draft. Both gating. Plus red-teaming for hallucination, leakage, and prompt injection through attorney-supplied documents
Skills
- 7+ years, or equivalent demonstrated depth
- Remote (US)
- Core working hours aligned with 9am–5pm Central
- 7+ years in software/ML engineering, or equivalent demonstrated depth , including substantial production AI/LLM systems work — not only notebooks, and not only fine-tuning
- You've shipped agentic or tool-using LLM systems in production
- You've built an evaluation system someone actually trusted
- Retrieval systems you've both built and evaluated — and the ability to tell a retrieval failure from a generation failure
- Statistical literacy on messy problems
- Strong Python engineering
- Fluent with AI coding tools, and accountable for what they produce
- The temperament to say the numbers don't support shipping , with evidence, to a room that wants to ship — including when it's your own work on the line
- Core stack is Python, Postgres, and frontier LLM APIs , with a self-hosted tracing and evaluation platform
- You don't need a legal background — you need to be willing to sit with attorneys and learn what "wrong" means here, because it isn't obvious from the outside
- Nice to have: legal, medical or financial domains where output errors are consequential; PII detection and NER; self-hosted inference; structured generation and schema-constrained output; open-source or published work on agents or evaluation
Benefits
- Meaningful early equity
- Health, dental and vision
- PTO
- 401(k)
- Remote (US)
- Core working hours aligned with 9am–5pm Central
Company Overview