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What You’ll Do · Design and build training pipelines, data workflows, and model integration systems · Develop infrastructure that accelerates research iteration and reduces turnaround time · Build systems for data collection, curation, and preprocessing at scale · Create tools and automation that move experiments toward production readiness · Optimize data pipelines for reliability, performance, and observability · Collaborate with ML researchers to understand their needs and remove technical blockers · Work on model serving infrastructure and integration with the production framework · Write clean, well-tested code that maintains high engineering standards · Participate in code reviews and help raise the engineering bar across the team · Contribute to shared tools, infrastructure, and cross-role projects (20% Time) · Work with the dual-leadership model (Engineering Manager and Tech Lead) to understand priorities and technical direction · Document systems and decisions to support team knowledge sharing Must Have · Bachelor Degree in Computer Science, or related discipline · 3+ years of professional software engineering experience in building production ML systems, training infrastructure, or research platforms · Proficiency in Python, additional experience with at least one other systems language (C++, C#, Java, Rust, or Go) · Hands-on experience with PyTorch or TensorFlow in production or research environments · Experience building or maintaining ML training pipelines or data workflows · Familiarity with model deployment, inference optimization, or MLOps practices · Strong Communicator in English Nice To Have · Experience with distributed training systems or GPU-accelerated computing · Knowledge of data versioning, experiment tracking, or ML metadata management · Familiarity with containerization (Docker) and orchestration tools · Contributions to open-source ML projects or research publications · Experience working in small, high-performance technical teams · Background in startups, high-growth environments, or consumer product companies · Passion for pushing technical boundaries and deep problem-solving
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.
