A machine learning engineer position is open, fully remote, paying $142,000 a year to candidates anywhere. This role sits within AI and machine learning specifically, with a focus on building systems that actually run in production, not on research or experimentation for its own sake.
Plenty of models get built and validated successfully without ever making it into a real product. This role exists specifically to close that gap: taking something that works on a data scientist's machine and turning it into a system that reliably serves predictions at scale to actual users.
What the role does
- Design, build, and deploy scalable ML systems
- Optimize model performance and infrastructure
- Collaborate with data scientists to move models from prototype to production
The gap between a model that works in a notebook and one that works in production is where most of this job actually happens. A recommendation model can perform beautifully in offline evaluation, then quietly degrade weeks after launch because the live data feeding it has drifted away from what it was trained on, and nobody notices until conversion rates start slipping for reasons that don't show up in any error log. Catching that kind of silent decay, through proper monitoring rather than waiting for a business metric to flag it, is core to the role.
Performance optimization often means solving a very different problem than the one a data scientist originally handed off. A model that takes 800 milliseconds per inference might be perfectly acceptable for a batch job, but completely unworkable for a real-time feature with a 100 millisecond latency budget, and getting from one to the other usually means restructuring how the model serves predictions rather than retraining it from scratch.
Infrastructure decisions shape much of what's possible here, too. Choosing between serving predictions through a dedicated model server, a batch pipeline, or an on-demand function isn't just a technical preference; it determines cost, latency, and how quickly a model can be updated once it's live. Getting that choice right up front avoids a painful migration later once traffic patterns become clear.
What's required
A bachelor's degree covers the education requirement, most often in computer science, engineering, or a closely related field. Candidates need 30 months of demonstrated experience building and deploying machine learning systems, and strong software engineering fundamentals are expected alongside ML-specific expertise, not treated as a separate, lesser skill set.
- Python
- Machine learning frameworks
- Data structures and algorithms
- SQL
- Cloud platforms
- Model deployment
- Software engineering practices
Hands-on MLOps experience, particularly with tools like MLflow or Kubeflow for managing the model lifecycle, will stand out clearly against candidates who've only trained models without operationalizing them. Familiarity with distributed training for larger models, experience building or maintaining a feature store, and any recent work with large language models will all strengthen an application, since the field has moved quickly enough that recent hands-on exposure matters more than it used to.
Containerization experience with Docker and Kubernetes, specifically for serving models, is worth calling out separately from general infrastructure familiarity, since packaging a model with its exact dependency versions and serving it reliably under load is a specialized skill distinct from general software deployment.
Pay and benefits
The role pays $142,000 annually. The position is fully remote with no location restrictions, and the standard package includes 401(k) matching, time off, and medical coverage. A training and conference budget is also included, which matters in a field where staying current requires genuinely ongoing learning rather than a one-time credential.
- Remote-work flexibility
- 401(k) matching
- Paid time off
- Medical coverage
- Training and conference budget
Where the job actually sits
Machine learning engineering occupies a specific middle ground that's easy to misunderstand from the outside. Naukri Mitra sees a lot of confusion between this role and a data scientist position, and the distinction matters: a data scientist typically owns the research and modeling decisions, while this role owns turning that work into something reliable, monitored, and scalable enough to run in production without constant hand-holding.
Working closely with data scientists means translating between two different mindsets. A data scientist optimizing purely for model accuracy might not weigh inference cost or latency the same way someone responsible for production infrastructure does, and part of this job is finding a model version that satisfies both the research goal and the operational constraints, rather than picking one over the other.
Model monitoring after deployment deserves its own mention, since it's easy to treat launch day as the finish line when it's really the starting point. Tracking prediction distributions over time, watching for the kind of drift that erodes accuracy gradually rather than all at once, and setting up alerts that actually get investigated rather than ignored are all part of keeping a model useful months after it first shipped.
Building toward this role
The remote salary for a machine learning engineer at this experience level reflects genuine demand for people who can do both ML and engineering well, not just one or the other. People figuring out how to become a remote machine learning engineer often come from a software engineering background and add ML skills, or from a data science background and build up production engineering skills, since either path can arrive at the same combination of abilities.
Candidates should come ready to discuss a specific model they took from prototype to production, including what broke along the way and how it got fixed. A GitHub repository or portfolio showing a deployed model, not just a training notebook, demonstrates the production half of this role in a way that a list of frameworks used never quite manages to. Someone who can describe a real monitoring alert they responded to, and what it turned out to mean, will generally stand out more than someone who can only speak to model architecture in the abstract.
