Deep Learning Engineer

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

• 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.✅ 𝗬𝗼𝘂'𝗿𝗲 𝗮 𝗚𝗿𝗲𝗮𝘁 𝗙𝗶𝘁 𝗜𝗳 𝗬𝗼𝘂
• 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.✨ 𝗪𝗵𝘆 𝗝𝗼𝗶𝗻 𝗨𝘀?
• 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 environment.