𝗔𝗯𝗼𝘂𝘁 𝘁𝗵𝗲 𝗷𝗼𝗯
This role is for one of our client companies — a VC-backed Artificial Intelligence startup that has raised $42M USD in funding.
𝗦𝗮𝗹𝗮𝗿𝘆: Up to 95LPA
Apply once and, if selected, get access to up to 20 remote and onsite interview opportunities.
🚀 𝗪𝗵𝗮𝘁 𝗪𝗲'𝗿𝗲 𝗕𝘂𝗶𝗹𝗱𝗶𝗻𝗴
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.
🚀 𝗗𝗲𝗲𝗽 𝗟𝗲𝗮𝗿𝗻𝗶𝗻𝗴 𝗘𝗻𝗴𝗶𝗻𝗲𝗲𝗿 (4+ 𝗬𝗲𝗮𝗿𝘀 𝗼𝗳 𝗘𝘅𝗽𝗲𝗿𝗶𝗲𝗻𝗰𝗲)
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.
🧩 𝗪𝗵𝗮𝘁 𝗬𝗼𝘂'𝗹𝗹 𝗗𝗼
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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.
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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.
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Develop Generalized Architectures: Build reusable models that generalize across datasets and tasks, reducing dependence on manual feature engineering and task-specific model customization.
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Advance Document Intelligence: Develop high-performance models for OCR, document classification, hierarchical information extraction, and structured extraction from complex documents and tables.
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Experiment with Emerging Techniques: Research and implement advancements in Transformer architectures, multimodal learning, representation learning, and model optimization to improve model capabilities.
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Drive Model Evaluation: Build robust evaluation frameworks, establish benchmarks, analyze failure cases, and systematically improve model accuracy, generalization, and reliability.
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Optimize Training & Inference: Improve training efficiency, GPU utilization, inference throughput, latency, and computational costs across the model lifecycle.
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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.
✅ 𝗬𝗼𝘂'𝗿𝗲 𝗮 𝗚𝗿𝗲𝗮𝘁 𝗙𝗶𝘁 𝗜𝗳 𝗬𝗼𝘂
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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.
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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.
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Foundation Models & Transformers: Strong understanding of Transformer architectures, attention mechanisms, and modern foundation models, including LLMs and Vision-Language Models (VLMs).
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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.
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Deep Learning Frameworks: Proficiency in PyTorch, TensorFlow, or JAX, with strong fundamentals in model architecture, representation learning, optimization, and evaluation.
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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.
✨ 𝗪𝗵𝘆 𝗝𝗼𝗶𝗻 𝗨𝘀?
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Build groundbreaking AI models with direct ownership from day one.
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Solve complex deep learning challenges that impact millions of users.
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Accelerate your career in a rapidly scaling Series B startup.
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Work alongside elite engineers in a highly collaborative environment.
