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1–3 jobs· newest first

Founding ML EngineerNew
a16z-speedrun · San Francisco, US
$130K–$150K
Speech and Audio Systems Engineer
Qualcomm · San Diego, US
$130K–$194KFull-time · Associate
Trainee
Michelin · Clermont-Ferrand, France
Internship · Intern

+10K new in the last hour

A

Founding ML Engineer

a16z-speedrun · Posted Sep 23, 2026

San Francisco, California, United States
$130K–$150K per year

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Own the AI systems at the heart of the product, building end‑to‑end ML pipelines and cloud architecture for predictive maintenance of commercial fleets.

Compensation

Early-stage compensation: 130 ~ 150k salary, ~1.5% experience and location - we're happy to talk through the details early in the process.

Responsibilities

Build data pipelines that move sensor data from vessel edge devices to cloud over satellite links
Stand up cloud architecture on AWS for real‑time and batch processing
Integrate historical maintenance records from CMMS databases
Develop ML models for anomaly detection and failure prediction using isolation forests, autoencoders, gradient‑boosted trees, and LSTMs
Design fleet‑wide and vessel‑specific modeling approaches

Skills

Machine Learning Data Engineering Cloud Architecture AWS MQTT Sparkplug B VSAT Starlink Encryption Anomaly Detection Isolation Forests Autoencoders Gradient‑Boosted Trees LSTMs

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Speech and Audio Systems Engineer

Qualcomm · Posted Nov 3, 2026

San Diego, California, United States
Full-time · Associate
$130K–$194K per year
2+ years

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Lead AI and signal processing research for speech enhancement and audio technologies in robotics and automotive applications.

Compensation

$129,500.00 - $194,300.00

Responsibilities

Advancing AI techniques for voice interaction in robotics and automotive
Conducting cutting-edge research in speech enhancement with linear and distributed microphone arrays
Using multi-microphone AI to detect talker count and directionality
Applying multi-rate and adaptive signal processing expertise
Prototyping and demonstrating research concepts

Requirements

Bachelor's degree in Engineering, Information Systems, Computer Science, or related field
Master's degree in Engineering, Information Systems, Computer Science, or related field
PhD in Engineering, Information Systems, Computer Science, or related field
Nice to have: Adobe Audition
Nice to have: IEEE publications

Skills

deep learning techniques CNNs RNNs autoencoders

Trainee

Michelin · Posted Nov 3, 2026

Clermont-Ferrand, Auvergne-Rhône-Alpes, France
Internship · Intern

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Responsibilities

Réaliser une revue de l’état de l’art des différentes méthodologies (autoencoders, variational autoencoders, méthodes de manifold learning et contrastive learning).
Implémenter et tester les méthodes.
Comparer les performances des différents modèles sur les données du modèle physique.
Évaluer les solutions les plus prometteuses sur les données industrielles.

Requirements

Master 2 in Applied Mathematics
Master 2 in Data Science
Master 2 in Deep Learning

Skills

Python PyTorch TensorFlow deep learning autoencoders variational autoencoders manifold learning contrastive learning

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advanced signal processing

multi-rate signal processing

adaptive beamforming

distributed microphone array processing

Benefits

competitive annual discretionary bonus program
RSU grants
highly competitive benefits package

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