Introduction: A Career at HARMAN Automotive
We’re a global, multi-disciplinary team that’s putting the innovative power of technology to work and transforming tomorrow. At HARMAN Automotive, we give you the keys to fast-track your career.
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Engineer audio systems and integrated technology platforms that augment the driving experience
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Combine ingenuity, in-depth research, and a spirit of collaboration with design and engineering excellence
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Advance in-vehicle infotainment, safety, efficiency, and enjoyment
About the Role
As an ML Engineer on the Innovation Team, you will design and operate end-to-end machine learning systems that continuously improve in-vehicle audio and infotainment experiences. You will transform raw and in-field data into production-ready solutions and ensure seamless integration, monitoring, and iteration of models deployed on embedded platforms.
You will participate in the full lifecycle from training to inference, collaborating with Data Scientists, DSP Engineers, and Audio Experts to design models and audio features. Your main responsibilities include ensuring optimal model performance on embedded devices and enabling remote updates to provide continuous improvements based on new training data or enhanced features.
You will enable a closed-loop system by integrating telemetry, model performance monitoring, and continuous retraining or system improvements based on real-world usage. While adhering to strict privacy, safety, and reliability requirements.
What You Will Do
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Deploy ML models on embedded devices such as ARM processors, GPUs, NPUs, SHARC, and other DSP architectures.
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Optimize models for edge hardware (e.g., quantization, pruning, distillation) to ensure real‑time execution within strict latency, memory, and compute budgets.
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Collaborate with Data Scientists and DSP Engineers to convert research notebooks into robust, testable training and inference code; define evaluation criteria, acceptance thresholds, and quality guardrails.
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Build end-to-end ML systems, including telemetry ingestion from vehicles, data validation, feature processing, training, evaluation, packaging, and deployment to production environments.
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Implement continuous training and continuous deployment (CT/CI/CD for ML) with full traceability of model and version registries, along with safe rollback mechanisms.
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Partner with Embedded DSP Engineers to ensure real‑time execution performance, leveraging hardware accelerators such as SIMD, MMA or NEON units.
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Estimate and measure model‑footprint metrics (CPU utilization, memory usage, latency, etc.).
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Redesign or restructure model architectures to reduce embedded resource consumption while maintaining similar levels of accuracy and overall model performance.
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Collaborate on OTA and data‑collection strategies to support continuous model improvement while adhering to strict privacy constraints.
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Contribute to internal documentation, reusable components, and best practices; mentor peers on MLOps workflows and edge optimization techniques.
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Implement monitoring systems to track model performance, data drift, and system behavior in production, ensuring reliability and continuous improvement based on real-world usage.
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Design and implement monitoring systems to track model performance, data drift, and system behavior in production, ensuring reliability and continuous improvement based on real-world usage.
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Provide guidance to less‑experienced team members on model deployment strategies and automation of ML pipelines.
What You Need to Be Successful
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Ability to work in rapid prototyping environments; self‑driven, fast learner, with a strong passion for innovation and problem solving with strong sense of ownership to reach goals on time.
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Bachelor’s or Master’s degree in Computer Science, Electrical Engineering, Machine Learning, Signal Processing, or a closely related field.
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5+ years of end‑to‑end ML development experience, including model training, optimization, deployment, and monitoring.
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Proficiency in C/C++ programming language for embedded systems
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Practical experience deploying models on embedded devices such as ARM Cortex cores, GPUs, NPUs, and DSPs (e.g., SHARC).
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Experience building MLOps pipelines, including automated training jobs, dataset versioning/switching, model optimization workflows, and unit tests for ML code.
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Strong understanding of real‑time systems execution constraints (scheduling, interrupts, shared‑core resource).
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Strong proficiency in Python and production‑grade machine learning using frameworks such as TensorFlow, PyTorch, scikit‑learn, NumPy, and Pandas.
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Experience with MLOps practices and CI/CD automation for ML model delivery.
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Experience working with data lakes and cloud data platforms (AWS S3, Azure, Databricks, etc.) for storage, processing, and training pipelines.
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Expertise in edge model optimization techniques including quantization (dynamic and post‑training), pruning, model compression, and mixed‑precision execution.
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Working knowledge of privacy‑preserving data handling (pseudonymization, anonymization, etc.), data‑quality checks, and data‑lineage tracking.
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Strong communication and collaboration skills, with the ability to work effectively in an intercultural and cross‑functional team.
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Proven ability to write unit tests, integration tests, and performance benchmarks for ML models and data pipelines.
Bonus Points if You Have
- Experience working with audio, image or signal processing domains
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Experience building data collection architecture design and overall Data Engineering infrastructure
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Experience working with automotive platforms such as Android Automotive OS, QNX, and embedded Linux.
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Understanding of audio‑hardware communication protocols such as TDM and I²S.
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Experience profiling and optimizing models on SoC or DSPS (Qualcomm, NXP, TI, NVIDIA SHARC, TI)
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Applied knowledge of audio signal processing such as STFT, mel‑spectrogram features, and filtering and experience using python audio libraries like Librosa, SciPy, or PyAudio.
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Familiarity with OTA update flows, artifact versioning, and safe rollback mechanisms.
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Experience building reproducible working‑environment (docker, virtual environments)
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Proven experience with formal software‑development processes and the Scrum framework.
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Experience connecting data cloud services directly on python
What Makes You Eligible
Ability to work from an office in Queretaro, 3+ days per week (hybrid)
Because the team operates globally, flexibility in working hours is required.
Successfully complete a background investigation and drug screen as a condition of employment
What We Offer
Access to employee discounts on world-class Harman and Samsung products (JBL, HARMAN Kardon, AKG, etc.)
Extensive training opportunities through our own HARMAN University
Competitive wellness benefits
Tuition reimbursement
“Be Brilliant” employee recognition and rewards program
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