Applied Machine Learning Engineer (Recommendation Systems)

Location: Boston, United States; hybrid

Company

hirify.global was spun out of MIT CSAIL and builds general-purpose AI systems that run efficiently across data center accelerators and on-device hardware. The company partners with enterprises in consumer electronics, automotive, life sciences, and financial services.

What you will do

• Own enterprise customer engagements involving recommendation and ranking workloads from requirements through delivery and evaluation.
• Translate customer requirements into specifications for recommendation models and communicate business metrics to technical decisions.
• Design data pipelines for user interaction data, feature engineering, and training-data curation at scale.
• Fine-tune and adapt large-scale sequential recommendation models, including HSTU-style architectures, for customer use cases.
• Design task-specific evaluations covering ranking quality, latency, throughput, offline metrics, A/B testing, and business metric alignment.
• Build reusable applied tooling and workflows for future customer engagements.

Requirements

• Hands-on experience building or fine-tuning recommendation models at scale, beyond off-the-shelf collaborative filtering.
• Experience with sequential recommendation architectures, user behavior modeling, or large-scale ranking systems.
• Strong intuition for data quality and evaluation design in recommendation contexts, including offline metrics, A/B testing, and business metric alignment.
• Experience with large-scale data pipelines for user interaction data and feature engineering.
• Proficiency in Python and PyTorch, with autonomous coding and debugging ability.
• Ability to independently own and deliver enterprise recommendation system engagements with minimal oversight.
• Ability to act as a trusted technical owner and make tradeoffs between model quality, latency, and business impact.
• Ability to build reusable applied workflows or tooling that accelerate future customer engagements.

Nice to have

• Experience with transformer-based recommendation architectures such as HSTU, SASRec, BERT4Rec, or similar.
• Experience delivering recommendation systems to external customers with measurable business outcomes.
• Familiarity with serving recommendation models under latency and throughput constraints.

Culture & Benefits

• Work on frontier machine learning systems and large-scale recommendation models for enterprise customers under production constraints.
• Competitive base salary with equity in a unicorn-stage company.
• 100% coverage of medical, dental, and vision premiums for employees and dependents.
• 401(k) matching of up to 4% of base pay.
• Unlimited paid time off and company-wide Refill Days throughout the year.