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- Remote
- Full\-time
- Engineering
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Powerfront Inc. (www.powerfront.com) isn't just another SaaS provider; we're the architects of the INSIDE™ Ai Customer Visual Engagement Platform. An Ai powered solution used by most luxury brands allowing them to *see* their online world in real\-time. We're talking about live customer behavior tracking and the power to engage at the precise moment of impact. Forget static analytics—we're revolutionizing customer interaction for industry titans like LV, Gucci, Ferrari, Cartier, Valentino, Staples, Rooms To Go and Lenovo.
We’re a global, fully remote team fueled by passion and a shared obsession with cutting\-edge technology. We are fully committed to the future of Ai, with the majority of our development work focused on account Ai and intelligent customer engagement. We believe Ai is the foundation of modern customer experience and are dedicated to building solutions that leverage its full potential.We’re not just building software; we’re forging strategic partnerships that redefine customer engagement.
If you're driven to tackle complex challenges, thrive in a dynamic, collaborative environment, and want to leave your mark on the next generation of e\-commerce, Powerfront is your launchpad. This isn’t just a job; it’s a career\-defining opportunity to be part of a team transforming how the world connects with brands.
We are seeking a talented and experienced Senior Android App Developer to join our mobile development team. In this role, you will be responsible for designing, developing, and maintaining high\-quality Android applications, particularly those related to video functionalities, live streaming, and video calls, that provide exceptional user experiences. The ideal candidate will have strong expertise in Kotlin and Java, as well as modern development practices. Experience with Firebase and Node.js is a plus. You will work closely with cross\-functional teams, including product managers, designers, and QA engineers, to bring innovative mobile solutions to life.
Key Responsibilities
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1\. Design and Development: Design, develop, and maintain high\-quality Android applications using Kotlin and Java, with a focus on live chat, video\-related features, live streaming, and video calls.
2\. User Experience: Collaborate with designers to create engaging, intuitive, and visually appealing user interfaces that adhere to Android design guidelines, including video playback, streaming features, and video call interfaces.
3\. Code Quality: Write clean, efficient, and maintainable code, following best practices and coding standards.
4\. Feature Implementation: Translate product requirements into functional features, especially those related to live chat, video streaming, recording, playback, and video calls, ensuring alignment with business goals and user needs.
5\. Performance Optimization: Optimize application performance by identifying and addressing bottlenecks in memory usage, network calls, and rendering, particularly for video\-related apps and live streaming.
6\. Testing and Debugging: Perform thorough testing and debugging to identify and resolve issues, ensuring the app functions seamlessly across different Android devices and OS versions.
7\. Play Store Submissions: Manage the end\-to\-end process of app submissions and updates to the Google Play Store, including compliance with Google's guidelines.
8\. Collaboration: Work closely with other developers, QA engineers, designers, and product managers to deliver high\-quality features on time.
9\. Continuous Improvement: Stay updated on the latest trends and advancements in Android development, live streaming, and video call technologies, and contribute to process improvements and team knowledge.
10\. Mentorship: Provide guidance and mentorship to junior developers, sharing your expertise and helping them grow their skills.
11\. Documentation: Maintain clear and comprehensive documentation for the codebase, including code comments, technical documentation, and design documents.
12\. Troubleshooting and Support: Provide technical support for existing applications, troubleshooting issues and implementing timely fixes, especially for video and streaming features.
13\. Firebase and Node.js: Experience with Firebase (such as authentication, real\-time databases, and cloud functions) and Node.js is a plus.
Key Requirements
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1\. Education and Experience: Bachelor's degree in Computer Science, Software Engineering, or a related field (or equivalent work experience). Proven experience as an Android App Developer with a strong portfolio showcasing your work, particularly on video\-related apps and features.
2\. Programming Languages: Strong proficiency in Kotlin and Java.
3\. Android Frameworks: In\-depth knowledge of Android frameworks such as Android Jetpack, AndroidX, ExoPlayer, and other Android technologies.
4\. Live Streaming and Video Calls: Proven experience in developing live streaming and video call features, such as peer\-to\-peer video calls, broadcasting, and real\-time chat.
5\. API Integration: Experience working with RESTful APIs and third\-party libraries for data integration and app functionality, especially for video and streaming services.
6\. Play Store Submission: Familiarity with the app submission process and compliance with Google Play Store guidelines.
7\. Performance Optimization: Strong experience in optimizing app performance and resource usage, especially for video\-related apps and live streaming.
8\. Debugging and Testing: Proficiency in debugging and testing tools such as Android Studio, including unit and UI testing frameworks.
9\. Version Control: Experience with version control systems, particularly Git.
10\. Collaboration and Communication: Excellent collaboration and communication skills, with the ability to work effectively in a team environment.
11\. Attention to Detail: Detail\-oriented mindset with a focus on delivering high\-quality and reliable software.
12\. Problem\-Solving Skills: Strong problem\-solving skills and the ability to diagnose and resolve complex technical issues.
13\. Portfolio: A strong portfolio of existing apps, particularly in the video and streaming space, demonstrating your abilities and experience.
14\. Certification: Relevant certifications in Android development are a plus.
If you are passionate about Android development and have the skills and experience to excel in this role, especially in the realm of live chat, video\-related apps, live streaming, and video calls, we encourage you to apply and become an essential part of our team, delivering innovative and high\-quality Android applications.
Additional Desired Qualifications
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Summary of Benefits
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- A dynamic \& forward\-thinking organization revolutionizing retail technology
- A virtual workforce, remote but highly interactive and collaborative
- Competitive salary and benefits
- Be a part of an amazing culture with a high client and staff retention
- Take pride in partnering with the most prestigious brands in the world
- Career progression and longevity
How to Apply
- This role is currently advertised on our website at www.powerfront.com/jobs. If you're ready to make an impact and be part of a team that’s building the future of AI\-driven customer engagement, we’d love to hear from you.
Note: The purpose of this profile is to provide a general summary of essential responsibilities for the position and is not meant as an exhaustive list. Assignments may differ for individuals within the same role based on business conditions, departmental need or geographic location.
Powerfront provides equal employment opportunities (EEO) to all employees and applicants for employment without regard to race, color, religion, sex, national origin, age, disability or genetics.
Role Details
About This Role
AI/ML Engineers build and deploy machine learning models in production. They work across the full ML lifecycle: data pipelines, model training, evaluation, and serving infrastructure. The role has evolved significantly over the past two years. Where ML Engineers once spent most of their time on model architecture, the job now tilts heavily toward inference optimization, cost management, and integrating LLM capabilities into existing systems. Companies want engineers who can ship production systems, and the experimenter-only role is fading fast.
Day-to-day, you're writing training pipelines, debugging data quality issues, setting up evaluation frameworks, and figuring out why your model performs differently in staging than it did on your dev set. The best ML engineers are obsessive about reproducibility and measurement. They instrument everything. They know that a model is only as good as the data feeding it and the infrastructure serving it.
Across the 4,317 AI roles we're tracking, AI/ML Engineer positions make up 70% of the market. At Powerfront Inc, this role fits into their broader AI and engineering organization.
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
What the Work Looks Like
A typical week might include: debugging a data pipeline that's silently dropping 3% of training examples, running A/B tests on a new model version, writing documentation for a feature flag system that lets you roll back model deployments, and reviewing a junior engineer's PR for a new evaluation metric. Meetings tend to be cross-functional since ML touches product, engineering, and data teams.
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
Skills in Demand for This Role
Python and PyTorch dominate the requirements. Most roles expect experience with cloud platforms (AWS, GCP, or Azure) and familiarity with ML frameworks like TensorFlow or JAX. RAG (Retrieval-Augmented Generation) has become a top-3 skill requirement as companies integrate LLMs into their products. Docker and Kubernetes show up in about a third of postings, reflecting the production focus of the role.
Beyond the core stack, employers increasingly want experience with experiment tracking tools (MLflow, Weights & Biases), feature stores, and vector databases. Fine-tuning experience is valuable but less common than you'd think from reading Twitter. Most production LLM work is RAG and prompt engineering, not fine-tuning. If you have both, you're in a strong position.
Companies that are serious about AI/ML hiring tend to post specific infrastructure details in the job description: the frameworks they use, their model serving stack, their data pipeline tools. Vague postings that just say 'ML experience required' without specifics are often companies that haven't figured out what they need yet.
Compensation Benchmarks
AI/ML Engineer roles pay a median of $214,900 based on 6,420 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $227,400.
Across all AI roles, the market median is $215,000. Top-quartile compensation starts at $266,300. The 90th percentile reaches $320,790. For comparison, the highest-paying categories include AI Safety ($287,500) and Research Engineer ($272,100). By seniority level: Entry: $110,000; Mid: $194,400; Senior: $227,400; Director: $274,554; VP: $241,000.
Powerfront Inc AI Hiring
Powerfront Inc has 6 open AI roles right now. They're hiring across Prompt Engineer, AI/ML Engineer. Based in Remote, US.
Remote Work Context
Remote AI roles pay a median of $180,000 across 1,196 positions. About 15% of all AI roles offer remote work.
Career Path
Common paths into AI/ML Engineer roles include Data Scientist, Software Engineer, Research Engineer.
From here, career progression typically leads toward ML Architect, AI Engineering Manager, Principal ML Engineer.
The fastest path into ML engineering is through software engineering with a self-directed ML education. A CS degree helps, but production engineering skills matter more than academic credentials. Build something that works, deploy it, and measure it. That portfolio project is worth more than a Coursera certificate. For career growth, the fork comes around the senior level: go deep on technical complexity (staff/principal track) or move into managing ML teams.
What to Expect in Interviews
Expect system design questions around ML pipelines: how you'd build a training pipeline for a specific use case, handle data drift, or design A/B testing infrastructure for model deployments. Coding rounds typically involve Python, with emphasis on data manipulation (pandas, numpy) and algorithm implementation. Take-home assignments often ask you to build an end-to-end ML pipeline from raw data to deployed model.
When evaluating opportunities: Companies that are serious about AI/ML hiring tend to post specific infrastructure details in the job description: the frameworks they use, their model serving stack, their data pipeline tools. Vague postings that just say 'ML experience required' without specifics are often companies that haven't figured out what they need yet.
AI Hiring Overview
The AI job market has 4,317 open positions tracked in our dataset. By seniority: 138 entry-level, 2,071 mid-level, 1,655 senior, and 453 leadership roles (Director, VP, C-Level). Remote roles make up 15% of the market (635 positions). The remaining 3,657 roles require on-site or hybrid attendance.
The market median for AI roles is $215,000. Top-quartile compensation starts at $266,300. The 90th percentile reaches $320,790. Highest-paying categories: AI Safety ($287,500 median, 34 roles); Research Engineer ($272,100 median, 227 roles); AI Engineering Manager ($244,000 median, 23 roles).
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
The AI Job Market Today
The AI job market spans 4,317 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (3,004), Data Scientist (345), AI Software Engineer (309). These three account for the majority of open positions, though smaller categories often have higher per-role compensation because of specialized skill requirements.
The seniority mix tells a story about where AI teams are in their maturity. Entry-level roles (138) are outnumbered by mid-level (2,071) and senior (1,655) positions, reflecting that most companies are past the 'build a team from scratch' phase and need experienced engineers who can ship production systems. Leadership roles (Director, VP, C-Level) total 453 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 15% of all AI roles (635 positions), with 3,657 requiring on-site or hybrid attendance. The remote share has stabilized after the post-pandemic correction. Senior and specialized roles (Research Scientist, ML Architect) are more likely to be remote-eligible than entry-level positions, partly because experienced hires have more negotiating power and partly because these roles require less hands-on mentorship.
AI compensation is structured in clear tiers. The market median sits at $215,000. Top-quartile roles start at $266,300, and the 90th percentile reaches $320,790. These figures include base salary with disclosed compensation. Total compensation (including equity, bonuses, and sign-on) runs 20-40% higher at companies that offer those components.
Category matters for compensation. AI Safety roles lead at $287,500 median, while Prompt Engineer roles sit at $145,000. The spread between highest and lowest-paying categories reflects the premium on specialized technical skills versus broader analytical roles.
The most in-demand skills across all AI postings: Python (2,249 postings), Aws (1,224 postings), Azure (938 postings), Rag (915 postings), Gcp (660 postings), Pytorch (640 postings), Prompt Engineering (624 postings), Kubernetes (559 postings). Python dominates, appearing in the vast majority of role descriptions regardless of category. Cloud platform experience (AWS, GCP, Azure) is the second most common requirement. The newer entrants to the top skills list (RAG, vector databases, LLM APIs) reflect the shift from traditional ML toward generative AI applications.
Frequently Asked Questions
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