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This role is for one of our client companies — a VC-backed Artificial Intelligence startup that has raised $42M USD in funding.

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Apply once and, if selected, get access to up to 20 remote and onsite interview opportunities.

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CodeRound AI matches the top 5% tech talent with the fastest-growing, VC-funded AI startups across Silicon Valley and India.

Top-tier product startups across the US, UK, EU, UAE, and India have hired top engineers through CodeRound.

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We are seeking a Deep Learning Engineer to build, fine-tune, and deploy state-of-the-art foundation models for complex multimodal and document intelligence challenges. If you want to go beyond API integrations to design and scale production-grade AI, we want to hear from you.

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- Build State-of-the-Art Models: Design, train, and deploy advanced deep learning architectures to solve complex business problems involving unstructured data and intelligent information extraction.
- Train & Fine-Tune Foundation Models: Lead hands-on experimentation with LLMs, VLMs, and specialized deep learning models using pre-training, fine-tuning, and parameter-efficient adaptation techniques.
- Develop Generalized Architectures: Build reusable models that generalize across datasets and tasks, reducing dependence on manual feature engineering and task-specific model customization.
- Advance Document Intelligence: Develop high-performance models for OCR, document classification, hierarchical information extraction, and structured extraction from complex documents and tables.
- Experiment with Emerging Techniques: Research and implement advancements in Transformer architectures, multimodal learning, representation learning, and model optimization to improve model capabilities.
- Drive Model Evaluation: Build robust evaluation frameworks, establish benchmarks,



analyze failure cases, and systematically improve model accuracy, generalization, and reliability.
- Optimize Training & Inference: Improve training efficiency, GPU utilization, inference throughput, latency, and computational costs across the model lifecycle.
- Deploy Models at Scale: Take models from experimentation to production by developing scalable serving pipelines, integrating ML infrastructure, and ensuring reliable performance under real-world workloads.

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- Deep Learning Experience: 4–10+ years of experience in Machine Learning/Deep Learning, with at least 2–3 years of hands-on experience training or fine-tuning deep learning models.
- Hands-on Model Training: Demonstrated experience with pre-training, continued pre-training, supervised fine-tuning (SFT), parameter-efficient fine-tuning (PEFT), LoRA/QLoRA, knowledge distillation, or domain adaptation.
- Foundation Models & Transformers: Strong understanding of Transformer architectures, attention mechanisms, and modern foundation models, including LLMs and Vision-Language Models (VLMs).
- Specialized Deep Learning Expertise: Strong hands-on experience in at least one domain, such as NLP, computer vision, OCR/document AI, multimodal learning, information retrieval, or speech/audio processing.
- Deep Learning Frameworks: Proficiency in PyTorch, TensorFlow, or JAX, with strong fundamentals in model architecture, representation learning, optimization, and evaluation.
- Model Evaluation & Optimization: Experience designing evaluation pipelines, benchmarking models, analyzing datasets, and improving metrics such as accuracy, F1 score, recall, precision, or word error rate.

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- Build groundbreaking AI models with direct ownership from day one.
- Solve complex deep learning challenges that impact millions of users.
- Accelerate your career in a rapidly scaling Series B startup.
- Work alongside elite engineers in a highly collaborative setting.

📌 Deep Learning Engineer (India)
🏢 CodeRound AI
📍 India