Why This Job is Featured on The SaaS Jobs
This Senior Machine Learning Engineer role stands out in the SaaS landscape because it is explicitly oriented toward taking modern AI techniques, including generative and agentic systems, into real product constraints. Poppulo operates an enterprise-scale communications and workplace platform, which typically means ML work must perform reliably across diverse customer environments, governance expectations, and integration surfaces rather than living as isolated experimentation.
For a long-term SaaS career, the notable value is the end-to-end scope across model development, deployment, and operationalisation. Experience that spans data pipelines, MLOps, latency and cost trade-offs, and production integrations tends to transfer well across SaaS companies building AI features, whether in core product, platform, or applied research groups. The emphasis on proof of concepts tied to product strategy also reflects a common SaaS reality: demonstrating measurable impact before broad rollout.
This position is best suited to an ML practitioner who prefers ownership and pragmatic decision-making, balancing research awareness with delivery. It fits someone comfortable collaborating across product and engineering, and who enjoys refining systems over time as usage patterns and requirements evolve in enterprise SaaS settings.
The section above is editorial commentary from The SaaS Jobs, provided to help SaaS professionals understand the role in a broader industry context.
Job Description
Senior Software Engineer – Machine Learning
Introduction:
Are you searching for an opportunity to play a key role in driving the dramatic growth of a highly successful software company?
At Poppulo, we’re working on what’s next in communications and workplace technology. As a pioneer in this industry, we understand that meaningfully reaching every employee is hard. And so is managing office space in a hybrid world. And so is improving the customer and guest experience. We exist to make each of these things easier. We exist to bring harmony to our customers.
And we do that at enterprise scale. Our omnichannel employee communications, customer communications, and workplace experience platform is trusted by over 6,000 organizations today, reaching more than 35M employees and delivering content to 500,000+ digital signs.
We know there’s no such thing as a “perfect" candidate - we’re all a work in progress and are growing new skills and capabilities all the time. We encourage you to apply for a position with Poppulo even if you don’t meet 100% of the requirements. We believe in fostering an environment where there is a diversity of perspectives, in hopes that we can all thrive.
The Opportunity
We are looking for a Sr. Machine Learning (ML) Engineer with a strong sense of ownership. If you are an ML practitioner who thrives in an environment where innovation meets practical application to build disruptive products, this role is for you.
At Poppulo, we intend to redefine boundaries and reshape Employee Communications and Digital Signage through the power of cutting-edge machine learning and AI. We are not just building products; we are disrupting the status-quo with transformative solutions that challenge conventional thinking, create competitive edge and create new market opportunities.
This role is pivotal to the success of our AI team. It is the backbone of our ML initiatives. This role bridges the gap between research and production by leveraging cutting-edge AI technology into leading, world-class solutions.
As a Sr. Machine Learning (ML) Engineer, you are the key driver for innovative problem solving by combining technical excellence with creative application of ML. Your expertise will be crucial in uncovering hidden opportunities within product ideas, including the design and deployment of agentic AI systems that can reason, plan, and act autonomously within real-world product constraints. Additionally, you will stay at the forefront of emerging technologies, continuously evaluating new AI/ML frameworks, tools, and best practices to help guide the evolution of our software solutions.
Key Responsibilities
- Solve complex challenges with AI/ML: Design, develop new AI-powered products that deliver the product roadmap, including agentic AI solutions that orchestrate LLMs, tools, and workflows to solve multi-step problems autonomously.
- Implement ML lifecycle - from data engineering and model development to cloud-based deployment, integrations and operationalisation. Incl. MLOps
- Productionise full-stack AI/ML solutions: Translate emerging techs like GenAI & agenticAI architectures into innovative, practical solutions that transform customer experiences.
- Align with Product Strategy: Create proof of concepts at high cadence to demonstrate/validate potential solutions as per our product strategy., including rapid experimentation with AI agents and multi-step reasoning systems.
- Optimise Model and system performance: Fine-tune, optimise training and inference performances, including latency, cost, and reliability trade-offs in agent-based and LLM-driven systems.
- Wider collaboration: Partner with cross-functional teams to demonstrate and validate the impact of ML innovations before introducing them into the product ecosystem.
- Research Savvy: Staying up-to-date with SOTA and industry trends in AI/ML, with a strong awareness of advances in agentic systems, autonomous workflows, and multi-agent architectures.
Technical Skills / Competencies
- Strong AI/ML background: Expertise in designing, building and deploying real-world ML applications (Computer Vision, Classification, etc.)
- Strong foundation in GenAI: Deep understanding of generative models (LLMs, etc.), AI Agents, prompt engineering RAG, vector databases.
- Technical Expertise: Proficiency in ML/GenAI frameworks, databases, shell scripts and programming languages, including frameworks and tools commonly used for building and orchestrating AI agents.
- MLOps expertise: Hands-on experience in setting up MLOps on AWS, Azure and GCP.
- Full-stack expertise: Proficiency in data pipelines, distributed systems, APIs, web front-end, mobile apps, automated testing and cloud platforms (AWS, GCP, etc.), with experience integrating AI agents into production software systems.
- Exceptional problem-solving skills: Ability to simplify and breakdown complex technical and business challenges to create innovative and practical solutions.
- Team management: Experience in guiding teams to deliver high-impact solutions.
- Continuous learning: Ability to learn quickly and apply new technologies to solve problems practically.
Education & Experience
- Master’s or PhD in AI, Statistics, Computer Science or a related field
- At least 6 years’ experience in a building complex, distributed software and AI systems, including production ML or AI-driven decision systems
- Strong understanding of entire end-to-end software development lifecycle
Why Us?
- An excellent workplace culture
- Competitive salary
- Company performance-related bonus
- Medical insurance
- Flexible working hours
- Educational assistance
- In-house soft skills training
