---
title: 'Applied Machine Learning Engineer at Bjak'
canonical: 'https://feeny.ai/job/applied-machine-learning-engineer-bjak-ireland-aq4d4dr1wbt6'
type: 'job'
last_seen: '2026-10-16'
---

# Applied Machine Learning Engineer at Bjak 

- **Company:** [Bjak ](https://feeny.ai/companies/bjak)
- **Location:** Ireland
- **Employment:** full-time
- **Work type:** remote
- **Posted:** 2026-10-16
- **Last confirmed live:** 2026-10-16
- **Apply:** https://jobs.ashbyhq.com/bjakcareer/2f9c5101-0e69-446e-83c6-b72562743c0f

## Job description

## About

There are over 5 billion users using basic applications today such email, notes, tasks, calendar and they're not AI-native. Our mission is to build proactive applications for anyone in the world, who are not used to complex prompting. We aim to bring intelligence to conversations, errands, organising and workflows, with minimal to no prompting. Our first application is an agent-first mobile communication app. We believe building applications that users use daily for communication, coordination and collaboration needs will help integrate AI into their lives. The net effect is to eliminate misinformation, achieve low coordination costs, enable high quality tasks and collaboration at scale with minimal or little prompting.

## Role

Build and improve ML systems that power our AI-native applications. Work on classification, retrieval, ranking, personalization and evaluation, taking ownership from model development to production deployment. This role is for engineers who enjoy solving practical ML problems, improving model quality and shipping reliable systems used by real users.

Focus

- Build and improve ML models for classification, ranking, retrieval and personalization.
- Develop efficient classifiers and optimize model accuracy, precision, latency and cost.
- Improve search relevance and context retrieval using embeddings, hybrid search and reranking.
- Build personalization models that learn from user behavior while respecting privacy.
- Develop evaluation pipelines, labeled datasets and metrics to measure model quality.
- Fine-tune and adapt existing models when supported by sufficient data.
- Build and maintain ML training, inference and deployment pipelines.
- Debug production issues, monitor model performance and ship improvements based on real-world feedback.

Tech Stack

- Python
- PyTorch / JAX
- Embeddings, vector search and ranking models
- Production ML systems and inference pipelines

Ideal Experience

- Strong foundations in machine learning and modern neural architectures.
- Hands-on experience building and deploying ML models in production.
- Experience in classification, ranking, retrieval, recommendation systems or personalization.
- Understanding of model evaluation, experimentation and performance metrics.
- Comfortable writing production-quality code and working with real-world datasets.
- Able to balance model quality, latency, cost and reliability.
- Strong ownership, problem-solving skills

Outcomes

- ML models in production meet expected accuracy, relevance, latency and reliability targets.
- Classification and ranking improvements deliver measurable gains in model quality.
- Evaluation pipelines identify regressions and support reliable model releases.
- Training, inference and data pipelines are robust, reproducible and maintainable.
- Model improvements are driven by real-world feedback and measurable results.
- Collaborates effectively with engineering and product teams to deliver reliable ML-powered features.

## How We Work

The best products in the world are built by small, world-class teams. We are a high-talent-density, hands-on team. We make decisions collectively and move quickly, balancing high-quality work with continuous learning. Joining our team requires the ability to bring structure, exercise judgment and execute independently. Our goal is to put a truly magical product into the hands of our users.

Interview Process If there appears to be a fit, we'll reach out to schedule 3, but no more than 4 interviews. Applications are evaluated by our technical team members. Interviews will be conducted virtually and/or onsite. We value transparency and efficiency, so expect a prompt decision. If you've demonstrated the exceptional skills and mindset we're looking for, we'll extend an offer to join us.

## About Bjak 

## Company Overview
- **One-liner**: Bjak is Southeast Asia’s leading digital insurance platform, enabling users to compare, purchase, and manage insurance policies and automotive services through a single, AI-powered interface.
- **Entity Type**: Private (Seed Stage)
- **Headquarters**: Selangor, Malaysia
- **Founded**: 2019
- **Founders**: Not publicly available

## Core Business
- **Primary industry**: Insurtech / Financial Services (with a growing automotive service vertical)
- **Target customers**: B2C (individuals seeking insurance and car repairs), B2B (partnerships with 16 insurers and workshop networks)
- **Mission**: “To develop technology‑based solutions to improve financial inclusion.”

## Products & Services
- **Insurance Platform (SaaS / Marketplace)**: Compare quotes from 16 insurers, purchase policies, manage claims, and access 24/7 VIP support. Supported by an AI agent.
- **Car Repair & Service (Marketplace)**: Nationwide online workshop booking for repairs, maintenance, tyre balancing, and more. Includes pay‑later options (installments from RM10/month).
- **Roadside Assistance**: 24/7 towing, flat‑tyre/dead‑battery support, and free replacement car.
- **Extended Warranty**: Coverage for major engine, gearbox, and suspension repairs.
- **Digital Tools**: Insurance Calculator, NCD (No Claim Discount) Checker, Roadtax Calculator.

## Market Standing
- **Valuation/Market Cap**: Not disclosed (private); the founder has indicated the company is considering an IPO to expand into Europe.
- **Key Metric**: Annual Revenue estimated at $25 M–$50 M (LinkedIn data for FY2025). Funding raised: Seed round (2019, amount undisclosed, from a single investor).
- **Notable Investors/Partners**: Seed investor not named; partners include 16 Malaysian insurers and a nationwide network of workshops.
- **Growth Signals**: 8 M+ users across Southeast Asia; 32.2% YoY headcount growth (100 employees); 356 open positions (up 278.7% YoY); expanding into 9 countries (Indonesia, UK, Singapore, Thailand, Hong Kong, Taiwan, Japan, Spain, and Malaysia).

## Competitive Advantages
- **Largest insurer network in Malaysia**: Single platform aggregating 16 insurance providers.
- **Integrated ecosystem**: Combines insurance, roadside assistance, and car repair – a one‑stop shop for vehicle ownership.
- **AI & automation**: Uses AI agents to simplify policy selection and claims processes.
- **Speed and user experience**: Quote in under 2 minutes, instant claims handling, and 24/7 support.

## Strategic Focus
- **Geographic expansion**: Entering European markets, with an IPO under consideration.
- **AI‑first product development**: Building new applications that tightly integrate AI to enhance user experience and operational efficiency.
- **Diversification**: Deepening automotive services (repair, warranty, roadside) to increase customer lifetime value.
- **Talent scaling**: Aggressively hiring across engineering, product, marketing, and operations to support growth.

## Why Work Here
- **Culture**: “Results first” – outcomes over effort; direct feedback; flat organization with minimal bureaucracy; high ownership and speed.
- **Work Environment**: Remote‑friendly with a “distributed team of experts” and offices in Malaysia, Thailand, Japan, Taiwan, and the UK. Recruiting globally.
- **Engineering Focus**: Lean, autonomous teams that prioritise product excellence and swift delivery.
- **Career Growth**: Rapidly scaling company with 356 open roles (including Lead, Founder’s Office, iOS Engineer, DevOps, ML Engineer) – opportunities for impact and advancement.
- **Note on Employer Rating**: Current Glassdoor-style rating is 2.3/5 (198 reviews) – potential concerns around work‑life balance and compensation. Candidates should verify recent employee sentiment during interviews.

## Sources
1. [bjak.my](https://bjak.my/en) – Official website (product overview, mission)
2. [bjak.my/en/career](https://bjak.my/en/career) – Career page (culture, values)
3. [jobs.ashbyhq.com/bjakcareer](https://jobs.ashbyhq.com/bjakcareer) – Active job listings and team descriptions
4. [linkedin.com/company/bjak](https://my.linkedin.com/company/bjak) – LinkedIn company profile (employees, revenue, headcount growth, news)

## Other roles at Bjak 

- [Machine Learning Engineer, Applied AI](https://feeny.ai/job/machine-learning-engineer-applied-ai-bjak-singapore-987qf3vsd0zq) — Singapore
- [Machine Learning Engineer, Applied AI](https://feeny.ai/job/machine-learning-engineer-applied-ai-bjak-china-bxsrdkd66hgr) — China
- [Machine Learning Engineer, Applied AI](https://feeny.ai/job/machine-learning-engineer-applied-ai-bjak-united-kingdom-z98jw1k5egw9) — United Kingdom
- [Machine Learning Engineer, Applied AI](https://feeny.ai/job/machine-learning-engineer-applied-ai-bjak-poland-txjtz7jdgs74) — Poland
- [Machine Learning Engineer, Applied AI](https://feeny.ai/job/machine-learning-engineer-applied-ai-bjak-spain-k0m2rh4r7fws) — Spain
- [Machine Learning Engineer, Applied AI](https://feeny.ai/job/machine-learning-engineer-applied-ai-bjak-portugal-hh8shsw3jbmd) — Portugal
- [Machine Learning Engineer, Applied AI](https://feeny.ai/job/machine-learning-engineer-applied-ai-bjak-germany-8c0d05qv2tfw) — Germany
- [Machine Learning Engineer, Applied AI](https://feeny.ai/job/machine-learning-engineer-applied-ai-bjak-sweden-j9r4gpjzs7rv) — Sweden
- [Machine Learning Engineer, Applied AI](https://feeny.ai/job/machine-learning-engineer-applied-ai-bjak-ireland-8sssycs43rw7) — Ireland
- [Machine Learning Engineer, Applied AI](https://feeny.ai/job/machine-learning-engineer-applied-ai-bjak-switzerland-eg7mdqs91y3j) — Switzerland