Staff Engineer - Machine Learning
AI in this role
Organizations everywhere struggle under the crushing costs and complexities of “solutions” that promise to simplify their lives. To create a better experience for their customers and employees. To help them grow. Software is a choice that can make or break a business. Create better or worse experiences. Propel or throttle growth. Business software has become a blocker instead of ways to get work done.
There’s another option. Freshworks. With a fresh vision for how the world works.
At Freshworks, we build uncomplicated service software that delivers exceptional customer and employee experiences. Our enterprise-grade solutions are powerful, yet easy to use, and quick to deliver results. Our people-first approach to AI eliminates friction, making employees more effective and organizations more productive. Over 72,000 companies, including Bridgestone, New Balance, Nucor, S&P Global, and Sony Music, trust Freshworks’ customer experience (CX) and employee experience (EX) software to fuel customer loyalty and service efficiency. And, over 4,500 Freshworks employees make this possible, all around the world.
Fresh vision. Real impact. Come build it with us.
The Impact You Will Create
As a Staff Machine Learning Engineer, you will serve as the critical architectural bridge between cutting-edge Data Science research and massive-scale, product-ready implementation. You will move beyond standard feature delivery to define the technical vision and infrastructure that brings sophisticated algorithms to life. Your work will directly result in:
- Massive Scale & Reliability: Architecting and deploying robust ML APIs and pipelines capable of serving millions of requests with ultra-low latency and unwavering reliability.
- Engineering Excellence: Setting the gold standard for ML Engineering practices, MLOps, and system design across the organization.
- Accelerated AI Innovation: Transforming theoretical models into high-performance, production-grade systems, directly shrinking the time-to-market for complex ML business solutions.
- Cross-Organizational Multiplier: Acting as a strategic technical anchor, influencing cross-product architects, leading POCs, and mentoring teams to ensure tight technical alignment across all engineering groups.
Roles & Responsibilities
- End-to-End Pipeline Architecture: Architect, build, and manage comprehensive, highly scalable ML pipelines covering data pre-processing, model generation, automated deployment, cross-validation, and active feedback loops.
- ML Algorithm Implementation: Partner deeply with Data Scientists to translate complex, theoretical ML models and algorithms into high-performance, production-grade code.
- High-Performance Service Delivery: Design, develop, and deploy highly extensible ML API services rigorously optimized for low latency and massive scalability.
- Operational Intelligence & Observability: Devise and build advanced monitoring capabilities to track both engineering system health and ML model performance metrics (drift, accuracy, etc.) over the long term.
- Strategic Innovation & Architecture: Architect solutions from scratch, leading Proof of Concept (POC) initiatives across various tech stacks to validate optimal solutions for complex business challenges.
- Technical Leadership & Execution: Own the full lifecycle of feature delivery autonomously—from requirement gathering with product stakeholders to final deployment—while collaborating with cross-product architects to drive platform adoption.
- Experience: 9+ years of progressive, highly relevant experience in software engineering and machine learning development.
- Production Excellence: A proven, demonstrable track record of successfully architecting, building, and productionizing complex Machine Learning solutions at an enterprise scale.
- MLOps Mastery: Deep, practical experience with modern MLOps practices, ensuring seamless, automated, and secure model transitions from development and training into production environments.
- Education: A Bachelor’s or Master’s degree in Computer Science, Artificial Intelligence, Mathematics, or a related quantitative field.
Skills
- Core Programming: Expert-level Object-Oriented Programming (OOP) expertise in Python and Java.
- ML & Deep Learning Frameworks: Mastery of industry-standard ML libraries and Deep Learning frameworks, including PyTorch, Keras, TensorFlow, and TFServing.
- Foundational Engineering: Deep, advanced understanding of Data Structures, Algorithms (DSA), and complex, distributed System Design.
- Cloud & Infrastructure: Hands-on knowledge and proficiency with Cloud infrastructure and services (AWS highly preferred) for large-scale data processing and ML model hosting.
- Analytical Problem Solving: Exceptional analytical and debugging skills with a relentless focus on algorithmic optimization, resource efficiency, and resolving bottlenecks in distributed systems.
Please note this is a hybrid role from Bengaluru that requires an in-office presence 3 days / week (Tue-Thu).
At Freshworks, we have fostered an environment that enables everyone to find their true potential, purpose, and passion, welcoming colleagues of all backgrounds, genders, sexual orientations, religions, and ethnicities. We are committed to providing equal opportunity and believe that diversity in the workplace creates a more vibrant, richer environment that boosts the goals of our employees, communities, and business. Fresh vision. Real impact. Come build it with us.
How we rate this
Staff Engineer - Machine Learning at Freshworks rates 92 out of 100 for how much of the daily work is AI. That makes it Builds AI (AI Level 4 of 4). The level is about AI in the job, not seniority.
Builds AI. The job is building AI systems.
- ●●●● Builds AI80 to 100
- ●●●○ Works on AI60 to 79
- ●●○○ Uses AI40 to 59
- ●○○○ Little AI0 to 39
Levels come from how often the tools, models and workflows of the role are named in the posting itself. Open the description and count.
Prepare for this job
A free preview built only from this posting: what it asks for, what you could be asked in an interview, and how to adjust your resume.
Skills and AI tools this role asks for
Questions you could be asked
- How do you monitor a model once it's live, and how do you know it needs retraining?
- Walk me through how you've used PyTorch in your day-to-day work.
- What are the limits of TensorFlow that you've run into, and how did you work around them?
- What's a project where you used Keras hands-on?
- How would you decide a model or AI system is ready to ship?
Adapt your resume
- List these exact terms on your resume: ML Ops, PyTorch, TensorFlow, and Keras. An applicant tracking system matches the wording, not the idea.
- Attach one line of real, concrete experience to at least one of them — a tool named with nothing behind it rarely survives a human read.
- Lead with what you built, trained or shipped — this role is judged on the AI system itself, not the tools around it.
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