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About The Company HubSpot is a leading provider of inbound marketing, sales, and customer service software that helps businesses grow and succeed. Renowned for its innovative approach and customer-centric solutions, HubSpot empowers organizations of all sizes to attract, engage, and delight customers through integrated tools and intelligent automation. With a commitment to fostering a collaborative and inclusive work environment, HubSpot continuously invests in cutting-edge technologies and talent development to maintain its position at the forefront of the industry. About The Role As a Staff Machine Learning Engineer within the Predictive Models team at HubSpot, you will play a pivotal role in shaping the future of our AI-driven insights and recommendations. This team is part of the AI Foundations Group and collaborates closely with the Predictive AI Platform team to develop next-generation predictive models that enhance product capabilities and customer experiences. Your expertise will drive the design, development, and deployment of sophisticated machine learning solutions that impact millions of users worldwide. In this senior individual contributor role, you will leverage your deep knowledge of machine learning techniques to lead complex projects, provide strategic technical guidance, and mentor other engineers. You will be hands-on in coding and system architecture, ensuring scalable, reliable, and privacy-conscious models. Your work will directly influence product features, improve predictive accuracy, and open new avenues for AI integration across HubSpot’s ecosystem. This role demands a strategic thinker with a passion for innovation, a collaborative mindset, and a commitment to engineering excellence. You will be expected to stay abreast of the latest advancements in AI and ML, applying them effectively to real-world business challenges while fostering a culture of continuous improvement and technical mastery. Qualifications Proven track record of delivering high-impact, cross-team projects in machine learning or AI. Extensive hands-on experience with ML frameworks such as PyTorch, TensorFlow, or scikit-learn. Deep understanding of ML techniques including deep learning, transformers, transfer learning, regression, classification, ranking, and recommendation systems. Strong architectural skills to design scalable and maintainable ML solutions aligned with business needs. Experience analyzing offline and online metrics, with attention to privacy, bias, security, and model maintainability. Ability to provide strategic technical direction and mentor engineering teams. Excellent problem-solving skills with pragmatic decision-making capabilities. Strong communication skills, capable of collaborating with cross-functional teams and stakeholders. Advanced degree (Master’s or PhD) in Computer Science, Data Science, Machine Learning, or related fields is preferred. Responsibilities Lead the development and deployment of advanced predictive models that drive product insights and recommendations. Collaborate with cross-functional teams to translate business requirements into effective ML solutions. Design scalable and reliable ML architectures, considering privacy, bias mitigation, and security aspects. Mentor and guide engineering teams, fostering a culture of innovation and technical excellence. Analyze model performance using a variety of metrics, iterating to improve accuracy and robustness. Stay informed about the latest AI and ML research, applying new techniques to enhance existing solutions. Provide strategic technical guidance on major projects, ensuring alignment with organizational goals. Promote best practices in model development, testing, deployment, and maintenance. Support the integration of models into production environments, ensuring operational stability and scalability. Benefits Competitive salary and comprehensive health benefits. Generous paid time off and flexible work arrangements. Opportunities for professional growth and continuous learning. Collaborative and inclusive company culture. Access to cutting-edge technology and research initiatives. Wellness programs and employee assistance resources. Equal Opportunity HubSpot is an equal opportunity employer. We celebrate diversity and are committed to creating an inclusive environment for all employees. All qualified applicants will receive consideration for employment without regard to race, color, religion, gender, gender identity or expression, sexual orientation, national origin, genetics, disability, age, or veteran status. We encourage individuals from all backgrounds to apply and join our mission to transform the way businesses grow and succeed.
Here's how to pick the right one and stand out in your application.
144.883Jobs
31.687IN
81%EN
That number is real. WorkMundi's database shows 144,883 open engineer roles across the world. India has the most with 31,687 jobs, followed by the United States with 30,084. If you just finished reading one job ad and felt paralyzed by choice, you're not alone—but this scale is actually an advantage. It means you can afford to be selective.
Start by geography and language. The majority of engineer ads—117,837 of them—have the job posting text written in English. Use that as one filter, but remember: the ad text language tells you nothing about whether the role actually requires you to speak English day-to-day. Read the job description carefully. Then check which countries have the volume you're targeting. Singapore, Poland, and Australia round out the top five after India and the US.
Next, learn who's hiring. Accenture has posted 2,801 engineer roles. andurilindustries, speechify, and jobgether are also actively recruiting. If you're applying to one of these names, research their hiring patterns and interview style before you apply. That homework pays off.
When you interview, expect the question every engineer hears: 'Tell me about a time you had to debug a problem that wasn't in your job description.' Have a specific story ready—not a general one. Name the tools, the deadline pressure, and what you learned. Hiring managers listen for whether you see problem-solving as part of the role itself, not a favour.