New opportunity

AI Software Engineer

NXP Semiconductors · Hyderabad, India

About this role

The AI Software Engineer evaluates Kinara products by developing and optimizing AI workload scripts and benchmarking deep learning model deployments in Hyderabad, India. The role works with technical marketing, product and customer support teams to understand benchmark data requirements, then prepares analysis reports for engineering and marketing stakeholders.

Responsibilities

Explore and apply benchmarking methods and tools for computer vision, large language models (LLMs) and vision-language models (VLMs).
Develop AI workload flows in Python and C or C++, including low-level performance work, and measure latency, throughput, accuracy and power consumption.
Evaluate the performance of hardware and software systems and report the results.

Qualifications

BTech in ECE, CS, Machine Learning or a related field, with at least 4 years of experience.
Proficiency in Python for AI workload scripting and optimization, and C or C++ for low-level performance work; experience developing AI workload flows in C++ and Python.
Exposure to neural networks, AI/ML/deep learning models and training, including TensorFlow, Caffe and PyTorch. Familiarity with AI accelerators, LLM frameworks, inference servers, MLPerf and MLflow for computer vision benchmarking, and LLM benchmarks such as MMLU, HumanEval, GSM8K, ARC Challenge and GPQA.
Familiarity with CI/CD tools, GitHub and automation frameworks. Working knowledge of Linux environments and device driver internals is preferred.

For full details about this role, click “Apply on company site” to view the employer’s original posting.

Skills for this role

PythonCC++AI workload benchmarkingDeep learning model deploymentPerformance evaluationComputer visionLLMsVLMsAI acceleratorsInference serversMLPerfMLflowMMLUHumanEvalGSM8KARC ChallengeGPQALinuxDevice driversCI/CDGitHubAutomation frameworksNeural networksMachine learningDeep learningTensorFlowCaffePyTorch

Checking your profile…

Preparation suggestions, not the employer’s actual interview questions. Always check the original posting for current requirements.

Understand the role

What matters most

Benchmark Kinara products across model types
The role explores benchmarking methods for computer vision, LLM and VLM workloads to evaluate Kinara products. Prepare to explain how you would select a benchmark appropriate to each workload rather than treating all model types alike.
Build and optimize deployment workloads
The posting calls for deep-learning deployment and benchmarking scripts, with Python for workload scripting and C or C++ for low-level performance work. Prepare an example that distinguishes workflow changes from low-level optimizations.
Measure hardware and software performance
The role measures and reports latency, throughput, accuracy and power consumption, and evaluates hardware and software systems. Prepare to explain measurement conditions and what each metric reveals.
Turn benchmark needs into usable reports
The engineer works with technical marketing, product and customer support to understand data requirements, then prepares analysis reports for engineering and marketing. Prepare to show how you would state methods, results and limitations for both audiences.

Likely challenges to think through

Comparing results across unlike workloads
A likely difficulty is choosing meaningful comparisons when vision, LLM and VLM benchmarks measure different behavior. Prepare to explain workload selection and metric definitions before interpreting results; this is a preparation hypothesis, not a stated employer problem.
Balancing speed, quality and power
Optimizing a deployment may improve latency or throughput while changing accuracy or power consumption. As a preparation hypothesis, work through how you would measure and report those trade-offs rather than present one metric as the whole result.
Making benchmark findings actionable
Engineering and marketing may need different levels of technical detail from the same results. Prepare to distinguish measured findings from interpretation and to explain limitations without overstating conclusions; differing stakeholder needs are inferred from the stated reporting duties.

Practice your answers

Technical practice

How would you design comparable benchmarks for vision, LLM and VLM workloads when evaluating Kinara products?

What to coverThe role calls for exploring benchmark methods across all three workload types. Suggested preparation: explain how you would define representative tasks, hold test conditions constant, and choose quality and performance measures appropriate to each modality.

How would you measure and interpret latency, throughput, accuracy and power consumption?

What to coverAll four measures are named in the posting. Suggested preparation: describe measurement boundaries and repeatability, then explain trade-offs such as higher throughput accompanied by greater latency or power use.

When would you use Python versus C or C++ in an AI workload flow?

What to coverThe role specifies Python scripting and optimization alongside C or C++ low-level performance work. Suggested preparation: discuss how you would locate a bottleneck before optimizing it and verify that the change preserves workload behavior.

How would you prepare a deep learning model for deployment and check that its benchmark remains valid?

What to coverThe posting mentions model deployment, training exposure, and TensorFlow, Caffe and PyTorch. Suggested preparation: discuss model inputs and outputs, preprocessing consistency, and checks that deployment changes have not invalidated accuracy comparisons.

How would you separate accelerator performance from inference-server overhead?

What to coverThe role calls for familiarity with AI accelerators and inference servers and evaluation of hardware and software systems. Suggested preparation: distinguish workload execution from request handling and explain how you would compare system-level and component-level measurements.

How would you use MLPerf and MLflow in a computer-vision benchmarking workflow?

What to coverBoth are named in the posting for computer-vision benchmarking. Suggested preparation: distinguish benchmark methodology from experiment tracking, and explain how recorded configurations and results would support reproducible comparisons.

How would you select and interpret the listed LLM benchmarks?

What to coverThe posting names MMLU, HumanEval, GSM8K, ARC Challenge and GPQA. Suggested preparation: explain the different capabilities these tests probe and why scores need consistent evaluation settings and careful interpretation.

How would you investigate unexpectedly high inference latency on a Linux-based system?

What to coverLinux and device-driver knowledge are preferred, and the role involves low-level performance evaluation. Suggested preparation: describe a stepwise check of the workload, software stack and hardware-facing path, marking any driver-level investigation as conditional on available evidence.

How would you automate benchmark runs without losing comparability?

What to coverThe posting mentions CI/CD tools, GitHub and automation frameworks. Suggested preparation: explain how you would version workload code and configurations, capture results, and detect performance changes while accounting for run-to-run variation.

How would you turn benchmark results into a report for engineering and marketing stakeholders?

What to coverPreparing detailed analysis reports for both audiences is a stated duty. Suggested preparation: show how you would present test conditions, metric definitions, limitations and trade-offs so each audience can use the findings without overstating them.

Behavioural practice

Tell me about a time you clarified an ambiguous benchmark-data request from other teams.

What to coverThe role works with technical marketing, product and customer support to understand data requirements. Suggested preparation: use a real situation, the questions and actions you took to define the request, and the resulting decision or deliverable.

Describe a time you explained a performance trade-off to audiences with different technical needs.

What to coverThe posting calls for reports to engineering and marketing stakeholders. Suggested preparation: give a real situation, how you tailored the explanation without changing the underlying evidence, and what resulted.

Tell me about a time you investigated an unexpected benchmark or performance result.

What to coverThe role involves measuring workloads and evaluating hardware and software performance. Suggested preparation: describe the situation, how you checked the measurement and isolated possible causes, and the result, including any unresolved uncertainty.

Describe a time you evaluated an unfamiliar benchmarking method or tool.

What to coverExploring and adopting methods for vision, LLM and VLM workloads is part of the stated work. Suggested preparation: give a real situation, the criteria and tests you used to decide whether the method fit, and the outcome.

Sharpen your knowledge

Topics to prepare

Performance metrics and measurement
Posting: The role measures latency, throughput, accuracy and power consumption when evaluating hardware and software. Practice suggestion: Draft a benchmark plan that defines each metric, workload conditions and how results will be compared.
Computer vision benchmarking
Posting: Familiarity with MLPerf and MLflow for computer vision benchmarking is requested. Practice suggestion: Review what each named tool contributes, then outline a reproducible vision-model comparison without assuming access to Kinara hardware.
LLM and VLM evaluation
Posting: The role explores LLM/VLM benchmarking and names MMLU, HumanEval, GSM8K, ARC Challenge and GPQA. Practice suggestion: Make a table of what each named benchmark tests and explain why one score would not cover every evaluation need.
Deep learning deployment and inference
Posting: The role involves model deployment, neural networks, TensorFlow, Caffe, PyTorch, AI accelerators, LLM frameworks and inference servers. Practice suggestion: Trace a model from training output through deployment and inference, identifying where performance measurements belong.
System-level performance factors
Posting: Hardware/software performance evaluation is required; Linux and device driver internals are preferred knowledge. Practice suggestion: Explain how host software, an inference server and an accelerator could each affect measured latency, distinguishing hypotheses from measured results.

Skills to revise

Python workload scripting
Posting: Python proficiency is requested for AI workload scripting and optimization. Practice suggestion: Write a repeatable model-inference script that records input conditions, latency and throughput, then compare an optimized version against a baseline.
C or C++ performance work
Posting: C or C++ is requested for low-level performance work, with experience developing workload flows in C++ and Python. Practice suggestion: Implement a small C++ inference-workload loop, time its stages and explain one measured optimization.
Benchmark execution and analysis
Posting: The role benchmarks deployments and evaluates hardware and software systems. Practice suggestion: Run repeated measurements on an available system, record latency, throughput and accuracy, and state how any available power measurement was obtained; do not substitute an estimate for a measurement.
Stakeholder reporting
Posting: Detailed benchmark and analysis reports serve engineering and marketing stakeholders, informed by technical marketing, product and customer support requirements. Practice suggestion: Turn one benchmark comparison into a short report with workload conditions, results, limitations and questions to clarify with stakeholders.
Versioned benchmark automation
Posting: Familiarity with CI/CD tools, GitHub and automation frameworks is requested. Practice suggestion: Put a benchmark script and its run instructions under version control, then outline an automated repeat run that preserves results for comparison.

Your preparation checklist

Map the stated qualifications to evidence
Prepare a concise record of relevant education, years of experience, Python and C/C++ workload development, hardware/software performance evaluation, and work with neural networks or the listed frameworks. Include only evidence you can substantiate.
Rehearse a benchmark project walkthrough
Choose a real AI deployment or evaluation project, if available. Explain the model, hardware and software setup, workload scripts, optimization decisions, measurements, and what the results did and did not show.
Practice designing a workload evaluation
Sketch a proposed evaluation for a vision or LLM/VLM workload on an AI accelerator. Specify inputs, deployment flow, baselines, repeatable runs, and how you would compare systems; treat this as a practice exercise, not a claimed Kinara setup.
Prepare a measurement plan
Write a one-page plan for measuring latency, throughput, accuracy and power consumption, including test conditions and sources of measurement error. Be ready to explain which trade-offs matter for a given benchmark request.
Review benchmark and automation choices
Prepare brief notes on when MLPerf or MLflow could help with vision benchmarking and how you would select an LLM evaluation such as MMLU or HumanEval. Include a reproducibility approach using version control and automation, as the role mentions GitHub and CI/CD familiarity.
Draft a stakeholder-ready results summary
Create a sample report outline with methodology, results, limitations and conclusions. Practice explaining the same findings to engineering and marketing audiences without overstating what a benchmark establishes.
Bring questions about the evaluation work
Ask how technical marketing, product and customer support define benchmark data needs; which Kinara products and workloads are priorities; and what hardware access, power-measurement methods and report formats the team uses.

Availability last checked: 5 Nov 2026. Status can change after a check; confirm on the employer’s website. Report an issue · Our checking policy

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