Machine Learning Engineers bridge the gap between research and production — taking ML models from notebooks to scalable, monitored systems. These roles require both deep technical ML knowledge and strong software engineering fundamentals.
We don’t have exact machine learning engineer jobs right now, but these positions use similar skills. Click any to see details, compare your resume, and apply.
TalentProof
TalentProof
TalentProof
TalentProof
TalentProof
TalentProof
TalentProof
TalentProof is seeking a highly motivated and results-driven B2B Lead Generation Specialist to join our team. This commission-based role is ideal for an individual passionate about identifying and qualifying new business opportunities, acting as the initial point of contact between TalentProof and potential clients. You will play a crucial role in expanding our client base and contributing directly to our growth.
Responsibilities
Qualifications
Preferred Qualifications
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Data Scientists focus on analysis, experimentation, and model prototyping. ML Engineers focus on productionizing models — building inference pipelines, monitoring drift, optimizing latency, and scaling systems. ML Engineers typically need stronger software engineering skills.
Key tools include PyTorch/TensorFlow for training, MLflow/Weights & Biases for experiment tracking, Kubeflow/Vertex AI for orchestration, Docker/Kubernetes for deployment, and vector databases (Pinecone, Weaviate) for LLM applications.
Yes — filter by employment type to find internship, contract, or full-time ML roles. Many companies offer ML internships as a pathway to full-time positions, and your verified Skill Scorecard helps you stand out even without years of experience.
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