Peakflo with its simple API and one-click ERP integrations, allows businesses to streamline their invoice-to-cash and procure-to-pay processes. 100+ companies, from scale-ups to enterprises, use Peakflo each to:

• Save 2000 man-hours/month on finance ops
• Get paid faster on customer invoices by 15-25 days
• Cut vendor bill payment time by 50%
• Automate three-way matching

🚀 Who we are & What we’re building

Peakflo is a rapidly growing Agentic AI company. We are revolutionizing the way global finance teams work with our agentic workflows, and we are actively seeking exceptional talent across diverse disciplines to champion this transformation, uniting multidisciplinary expertise to propel our global strategic vision.

• Our Growth Story : Peakflo is backed by top-tier global accelerators and investors. We are proud alumni of the prestigious Y-Combinator (W22) and the Google AI Accelerator. Our momentum and impact have been recognized globally by top tech and finance publications:
  • TechCrunch Exclusive: Peakflo’s bid to build business payments for Southeast Asia attracts capital, customers
  • PYMNTS: Peakflo Raises $4.1M in Seed Funding to streamline vendor payments
  • Grit Daily: Latest Feature on Peakflo's Innovations

Our Culture : We believe in building a vibrant, high-performance culture that rewards curiosity, ownership, and innovation. Our team spans the globe, and we love coming together to solve hard problems and celebrate our wins. Most importantly, we have begun building an environment that provides the support and mentorship needed to succeed, learn, and grow. ❤️

💻 What we’re Looking For:

We are seeking a highly motivated and detail-oriented Machine Learning (ML) Engineer Intern to join our dynamic team. As a ML Engineer Intern, you will play a crucial part in developing and implementing machine learning solutions to drive business growth and improve our products.

💪 What you’ll do

• Craft voice‑optimized prompt flows:

  • Design conversational flows that account for natural speech patterns—pauses, interruptions, intonation—with goal‑oriented multi‑turn dialogue optimized for voice-only interactions.
  • Ensure prompts are clear for TTS pronunciation (e.g. spelling out email IDs, phone numbers, dates explicitly) to avoid ambiguity

• Implement agentic architecture and hierarchical workflows:

  • Build finance AI agents that coordinate sub‑agents—for example, a Research Agent to fetch financial data, a Finance Agent to analyze transactions, and an Editor Agent to craft reports.
  • Organize these into hierarchical-sequential or plan‑and‑execute flows for scalability and modularity

   

• Continuous prompt refinement & iteration:

  • Use LLM feedback loops or "self‑reflection" to score outputs, detect hallucinations, and improve prompts over time.
  • Set up pipelines for A/B testing, prompt versioning, and performance QA tailored to financial use cases 
  • Apply expertise in and potentially fine-tune leading LLMs (e.g., Google's Gemini, OpenAI's GPT series, Anthropic's Claude) to optimize AI Finance Employee performance.
  • Optimize overall LLM system performance to ensure low latency and high efficiency across all financial AI applications.

• Grounding & retrieval true‑fact enhancement:

  • Integrate RAG (retrieval-augmented generation) with enterprise knowledge bases or financial APIs to avoid misinformation or drift—especially for task‑sensitive use cases like invoicing or AR follow-ups.
  • Maintain tight context control around business domains to limit actions only to finance‑specific interactions

   

• Voice integration & prompt‑tech stack collaboration:

  • Collaborate closely with engineering teams to integrate prompts with speech recognition, intent extraction, LiveKit voice infrastructure, and telephony APIs.
  • Ensure client-side and server-side orchestration maintains real‑time responsiveness and low latency in voice flows
  • Architect and integrate LLM systems with a wide range of third-party tools and platforms to facilitate diverse use cases, including email interactions and user chat interfaces.

• AI Solution Development - Develop and optimize complementary AI components such as advanced customizable OCR models, intelligent chatbots, and automated approval systems to support financial workflows.

• Maintain a strong understanding of and stay current with the latest advancements, research, and best practices in large language model (LLM) technologies and AI to drive continuous innovation.

🕵️‍♀️ Who we’re looking for

• Bachelor's or Master's degree in Statistics, Machine Learning, Data Science, or a related field.
• 0.5 - 2 years of industry experience with Machine Learning, Statistics and / or LLM fine-tuning and prompt engineering.
• Excellent written and verbal communication skills in English.
• Extensive experience in Python programming.
• Proficiency with cloud platforms like Google Cloud.
• Strong expertise in Python back-end development and launching ML products in production.
• Passionate about AI and its potential to transform businesses.

➕ We’re Particularly Interested In People Who Have:

1. Experience with multiple LLM platforms and frameworks.
2. Familiarity with natural language processing (NLP) techniques and libraries.
3. Knowledge of software engineering best practices and version control systems (git)

🙂Benefits :

• Competitive stipend
• Performance based full-time role conversion
• Benefits package (post full-time conversion)
• Opportunity for career growth and skill development.
• Collaborative and innovative work environment.
• Flexible work hours and remote work options.

We are looking for quick-thinking, problem-solving full stack engineers to build the next generation of fintech products. Our SaaS product is built using typescript, node.js and react.

You should apply if you are interested in backend, microservice distributed architecture; if you are looking to join a high-growth high-pace startup with great engineering culture; if you want to work in a passionate high-skilled team.