Boam AI builds managed data solutions that transform messy, unstructured signals from public, private, and proprietary sources into structured, reliable, and always up-to-date intelligence on millions of SMBs and enterprises worldwide. These agentic systems power CRMs, data warehouses, AI products, and mission-critical decisions across the enterprise.
As a Staff Machine Learning Engineer, you will set the technical direction for how ML, LLMs, and agents power our platform. You will architect advanced ML systems, define standards for training and deployment, and own the reliability and quality of ML across the company. This is a role for someone who has led ML and LLM systems at scale, mentors senior engineers, and wants to shape both the architecture and roadmap of a small, senior team.
What You’ll DoSet ML technical direction and roadmap: architecture, standards, and the next platform bets
Architect ML systems that blend classic models with LLM and agentic patterns
Own ML + agent workflows for entity resolution, attribute extraction, and verification
Build the shared pipelines and tooling that run those workflows at scale
Define standards for model training, deployment, and evaluation
Own reliability and quality metrics: evals, drift, and regressions
Lead technical design for ML-powered agents across multiple teams
Drive cross-functional technical decisions on ML tradeoffs: quality, cost, latency, reliability
Guide adoption of new ML and LLM research into production
Mentor ML engineers, including seniors, through feedback and design reviews
8+ years ML or AI engineering experience, including production ownership
Have architected LLM or agentic systems in real production environments
Strong Python skills and deep experience with ML infrastructure, pipelines, and evaluation
Comfortable designing high-throughput distributed ML systems and data flows
History of mentoring senior AI engineers and guiding their growth
Experience defining AI technical strategy and roadmaps for teams or orgs
Comfortable evaluating and introducing new ML tools, frameworks, and patterns
Bias to ship, measure, and refine while investing in key foundations
Motivated by hard real-world data problems and critical workflows
Join a no-politics, high-trust, low-ego, high-talent team
Own mission-critical ML and agent systems for enterprise customers
Work directly with founders, Head of Engineering, and engineering leaders
High autonomy, clear ownership, and visible impact from day one
Operate at the intersection of AI, data, and enterprise workflows
Top-tier compensation with meaningful equity participation
Help shape the ML platform, patterns, and culture of the company
Our process is fast, structured, and transparent – built to respect your time and surface real mutual fit
1. Intro Call
Brief call to meet and outline the role
2. Deep Dive
Review past ML and LLM systems and key decisions
3. Architecture + Leadership Exercise
Tackle a Boam-style ML and agent design problem
4. Founder Conversation
Align on values, ambition, and ownership expectations
