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 an Applied AI Engineer, you will own the lifecycle of the models and agents that power our product. You will build and maintain ML data pipelines, develop production-ready LLM- and agent-driven features, and work closely with backend engineers to integrate models into real systems. This is a role for someone who has shipped ML to production, wants real ownership over models and pipelines, and is excited to work on a small, senior team where AI is at the core of the product.
What You’ll DoOwn the ML model lifecycle from training and evaluation to deployment
Build and maintain ML & agentic data pipelines for training, inference, and monitoring
Develop production-ready agentic and large-language-model–driven features
Integrate models into production systems in close collaboration with backend engineers
Implement experiment tracking, model CI/CD, and automated retraining
Improve performance, reliability, and observability of ML and AI systems in production
Work with product and data teams to turn ambiguous problems into concrete ML/AI solutions
Use next-gen AI tools to improve iteration speed and model quality
3+ years of ML engineering experience working on production systems
Strong Python skills and hands-on ownership of end-to-end ML pipelines or agentic systems
Comfortable with data preprocessing, feature engineering, and evaluation at scale
Familiarity with LLMs or modern foundation models
Experience with common ML tooling (e.g. PyTorch, TensorFlow, XGBoost, vector DBs, experiment trackers)
Comfortable working across APIs, data stores, and infrastructure, not just notebooks
Bias to ship, measure, and refine rather than chase perfect offline metrics
Motivated by solving real customer problems and seeing models used in the wild
Thrive without heavy process, QA buffers, or endless safeguards – you own what you ship
Join a no-politics, high-trust, low-ego, and high-talent team
Work on mission-critical ML/AI systems used by top-tier enterprise customers
Work directly with founders, the Head of Engineering, and senior engineers on problems that matter
High autonomy, real impact, and clear ownership from day one
Operate at the intersection of AI, data infrastructure, and enterprise workflows
Top-tier compensation with meaningful equity upside
Help shape the ML/AI platform, patterns, and practices you can be proud of
Our process is fast, structured, and transparent – built to respect your time and surface real mutual fit
1. Intro Call
A short conversation to learn more about you, share context on Boam AI and the ML/AI role, and answer initial questions
2. Deep Dive
Walk us through past ML/AI work, systems you have built, and how you think about complex, ambiguous modeling and production challenges
3. Work Sample
Solve a real Boam-style ML/AI challenge that shows your modeling approach, pipeline thinking, and execution muscle
4. Founder / Leadership Conversation
Candid discussion with our founder and Head of Engineering on ambition, values, ownership, and how you would help us scale our ML and agentic systems
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What We Do
boam is building the future of personalized restaurant discovery. The platform’s smart restaurant recommendation engine makes it easy for people to find their next meal. By analyzing millions of data points from the best review sites, critics, delivery platforms, and other tools, boam finds the best restaurant options for any meal.
