Job Specifications
Location: New York, NY (Hybrid)
Team: Engineering / Integrations
Level: Senior / IC Leader
Type: Contract to Hire
About the Role
We’re seeking a Senior Software Engineer who is, first and foremost, a world-class software engineer—someone who designs, builds, and ships production-grade systems—while also bringing deep, practical experience applying AI and ML to solve real business problems.
Boost is a fast-growing leader in the B2B payments space. In this high-impact role, you will help improve data accuracy, reduce manual intervention, and increase our straight‑through processing rate to accelerate company growth.
This is a thought-leadership and individual contributor position. You will help the Boost Engineering team determine how to tackle crucial business challenges and then guide those solutions through production. You will architect for scalable services, lead complex initiatives end-to-end, and mentor engineers across functions. You communicate crisply with both executives and technical teams, translate ambiguous business goals into technical strategy, and back decisions with data.
What You’ll Do
Architecture & Delivery
Own the technical vision and implementation of AI/ML‑enabled products and platforms (e.g., inference services, feature pipelines, model lifecycle tooling), driving solutions from design through production.
Design cloud-native, highly available systems (APIs, microservices, event‑driven workflows) with clear SLOs, robust observability, and strong CI/CD practices.
Hands-On Engineering
Write high-quality code in Python, Java, or TypeScript; contribute to frameworks and utilities; lead code reviews.
Build both batch and streaming data pipelines, optimize model inference paths, and harden ML integration points.
Applied AI/ML
Select and implement appropriate ML approaches (supervised/unsupervised learning, NLP, recommendation systems, time series models, anomaly detection, and RL where applicable). Operationalize models with attention to drift, performance, and cost.
Evaluate foundation models and classical ML techniques; determine when to use, tune, or build solutions based on data, latency, regulatory requirements, and ROI.
Data Analysis & Decisioning
Analyze product and operational data, design experiments, define success metrics, and build dashboards to guide prioritization and measure impact.
Translate insights into backlog recommendations, architectural improvements, or model retraining plans.
Technical Leadership & Communication
Influence architecture and strategy across engineering teams; lead design reviews and implementation efforts; write clear RFCs and executive-level updates.
Partner closely with Product, Data, Security, and Compliance teams. Communicate trade-offs to senior management with clarity and brevity.
Quality, Reliability, and Risk
Enforce coding standards and testing strategies (unit, integration, end‑to‑end) along with disciplined release practices.
Champion responsible AI: model monitoring, bias testing, governance, data privacy, and security.
Qualifications (Required)
8+ years building and operating production software; 2–4+ years applying AI/ML in shipped products (beyond research or POCs).
Proven experience delivering large-scale and robust systems and data‑intensive applications in AWS.
Strong hands-on experience with Postgres and DynamoDB.
Proficiency in Python and either Java or TypeScript; familiarity with infrastructure-as-code, containers, and orchestration.
Practical experience with ML pipelines (feature stores, training workflows, ML CI/CD, model registries), LLMs, and model serving/monitoring.
Advanced data analysis skills: experiment design, A/B testing, statistical analysis, and metric definition.
Exceptional communication skills: able to brief executives, align cross‑functional teams, and produce clear design docs and RFCs.
Preferred Experience
Payments industry experience (huge plus).
NLP (including RAG), recommendations, and anomaly detection in production systems.
Performance engineering: profiling, optimization, and cost/latency tuning for inference workloads.
Security, compliance, and governance for AI/ML systems (PII handling, auditability, access control).
Domain experience in payments, commerce, or integrations.
How You’ll Work
Lead by building: Set technical direction and personally deliver critical components.
Communicate to align: Tailor communication for executives, PMs, and engineers; explain complex topics in a clear, accessible way.
Decide with data: Define meaningful metrics, build dashboards, and use experimentation to validate improvements.
Compensation: $110/hour