Sigma360 is looking for an Applied AI Engineer to turn hard compliance problems into working AI systems. You'll train models, build agents, engineer prompts, and validate everything against real-world data. It's a notebooks-first role for a hands-on builder who's energized by ambiguity and obsessed with making AI actually perform on messy, adversarial data.
We're hiring exceptional engineers across the junior-to-senior spectrum; final compensation reflects experience and level.
About Sigma360
Sigma360 is an MIT-incubated, venture-backed, Series B data and analytics company helping financial institutions, fintechs, and governments manage entity risk. We turn the world's messy, fragmented data into clear answers — powering name screening, sanctions compliance, adverse media monitoring, KYC investigations, and risk research for some of the world's most demanding compliance teams.
Engineers own architecture, AI & data science own model quality, and everyone owns impact.
Why This Role Matters
Compliance AI is genuinely hard. Financial crime is adversarial — names, structures, and jurisdictions shift constantly. The data is messy, multilingual, and incomplete. The stakes are real: a missed sanctions hit is a regulatory event; a flood of false positives buries the analyst teams that rely on our product.
We're automating some of the most repetitive, highly regulated back-office work in global finance — and building the AI systems that make it possible:
Agents that reason about entities across fragmented data sources
Models that match names across dozens of languages and scripts
Classifiers that separate relevant adverse media from noise
Workflows that compress hours of analyst work into minutes
This isn't a "fine-tune an open-source model and call it a day" problem. It rewards creativity, scientific rigor, and a real tolerance for iteration. It's notebooks-first and research-oriented — upstream work, not production infrastructure. If you're energized by hard, ambiguous problems and don't need a detailed task list to make progress, you'll thrive here.
What You'll Do
Model development and AI system construction
Build new AI agents and agentic workflows — design tool calls, orchestrate multi-step reasoning, validate outputs end to end
Fine-tune and train language models, embedding models, and classifiers on proprietary compliance data
Engineer, test, and iterate on prompts for LLM-powered features — systematically measuring output quality and failure modes
Construct and curate high-quality training and evaluation datasets; ensure stratified, representative coverage
Validate AI systems against real-world edge cases before handoff to production engineering
Data science and evaluation
Design rigorous evaluation frameworks: define metrics, build holdout sets, measure precision/recall, identify distribution shift
Run data exercises to test and tune AI/ML systems — measure catch rate and false positive rate on real entity data, diagnose failure modes, iterate on filter configurations and thresholds
Answer analytical questions from product and business teams about AI feature performance, usage, and value
Tech Stack
Delta Lake / lakehouse; data shipped downstream to Postgres and Neo4j
Infrastructure:
AWS
Downstream consumers:
Golang backend, React frontend
What We're Looking For
Required
Python fluency
— you write clean, readable, maintainable code in notebooks every day; you're comfortable with pandas/pyspark and large DataFrames
ML fundamentals
— you understand model training, evaluation, overfitting, precision/recall tradeoffs; you can design a proper eval from scratch
LLM/AI systems experience
— you have built something real with language models: an agent, a RAG pipeline, a fine-tuned classifier, or something novel
Prompt engineering
— you treat prompts like code; you test, measure, and iterate systematically rather than vibe-checking outputs
Autonomy and intellectual curiosity
— you're energized by ambiguous problems; you make progress without a detailed task list
Bachelor's degree (or higher) in Computer Science, Machine Learning, Data Science, Statistics — or equivalent practical experience
Nice to Have
Databricks experience (notebooks, workflows, Delta)
NLP / NER background — multilingual entity recognition, text classification, information extraction
Experience training or fine-tuning embedding models
Agentic workflow design and debugging
AML, sanctions, KYC, or financial crime domain knowledge
Model risk management experience or familiarity with SR 11-7 standards
MLOps basics — experiment tracking, model registry, evaluation pipelines
What We Offer
Remote-first team with high autonomy and ownership — your work is visible and consequential
Competitive compensation and meaningful equity in a Series B company growing rapidly
Health, dental, vision, and other benefits (or local equivalent)
Generous time off and a culture that supports continued learning
Small team environment: you'll have real ownership and real influence from day one
How to Apply
Apply with your résumé and a short note on the hardest AI system you've built — what made it hard, and what you'd do differently today.
Sigma360 is an equal opportunity employer. We are committed to fair hiring practices and to creating a welcoming environment for all team members. All qualified applicants will receive consideration without regard to race, color, religion, gender, gender identity or expression, sexual orientation, national origin, disability, age, familial status, or veteran status.