Opportunity at
InMobiSDE IV (ML Engineering)
InMobi is hiring an SDE IV for ML Engineering in a full-time, on-site role. The job sits between data science and production software: the engineer turns…
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About This Role
InMobi is hiring an SDE IV for ML Engineering in a full-time, on-site role. The job sits between data science and production software: the engineer turns analytical findings from data scientists into systems that run live. The posting frames the work as hands-on engineering rather than pure research. Much of the job involves building finetuning and distillation pipelines, routing algorithms and cost-optimization logic that move into live services. Pipelines and…
Job Description
InMobi is hiring an SDE IV for ML Engineering in a full-time, on-site role. The job sits between data science and production software: the engineer turns analytical findings from data scientists into systems that run live. The posting frames the work as hands-on engineering rather than pure research.
Much of the job involves building finetuning and distillation pipelines, routing algorithms and cost-optimization logic that move into live services. Pipelines and platform parts are treated as owned products with clean interfaces, not disposable scripts. The engineer is also accountable for the quality of shared evaluation infrastructure, giving model and algorithm teams one dependable base to test against.
After release, the role includes monitoring and iterating on production systems and closing the loop with the data scientist about what works in practice. Routing and optimization problems, where cost, latency and unpredictable model output all constrain the design, are a central challenge. The role suits a strong software or ML engineer who can build from another team's analysis and who is ready to pick up finetuning and distillation as the work goes. Experience there is welcome but optional.
Responsibilities
- Turn data scientists' findings into production finetuning and distillation pipelines
- Build routing algorithms and cost-optimization logic for live systems
- Create data pipelines and platform components with real interfaces rather than one-off scripts
- Own the engineering quality of shared evaluation infrastructure
- Ship, monitor and iterate on production systems, reporting results back to data scientists
Requirements
- Strong software and ML engineering fundamentals, including production-grade code quality
- Track record of delivering pipelines and services to production
- Algorithmic thinking for routing and optimization where cost and latency are fixed constraints
- Comfort working from another team's analysis rather than originating every product idea
Qualifications
- Prior experience with model finetuning or distillation, a plus rather than a requirement
Core Skills
Frequently Asked Questions
Answers are based only on the employer’s listing; where it doesn’t say, neither do we.
Where is the role located?
The posting does not name a city. It is listed as an on-site position.
What experience is needed?
The posting asks for strong software and ML engineering fundamentals. Prior model finetuning or distillation experience is a plus rather than a requirement.
Is it full-time?
Yes, the role is listed as full-time.
What is the salary?
The posting does not specify a salary.
How do I apply?
The posting does not describe an application process.
Sample Interview Questions
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