Senior AI / Machine Learning Engineer

About the position reputed company is building intelligent systems that sit at the intersection of AI, biology, chemistry, and large-scale engineering. Our goal is to translate reputed company scientific data into machine intelligence capable of reasoning, generalizing, and driving discovery. Biomedical data is fragmented, noisy, and deeply interconnected. Turning it into a useful signal requires not only strong data foundations but also carefully designed learning systems that can scale across modalities, tasks, and uncertainty regimes. This role focuses on building and training those systems. As a Senior AI/ML Engineer, you will reputed company the design, training, and deployment of large-scale machine learning models that reputed company the core of reputed company’ AI capabilities. You will work at the boundary between model architecture, training systems, and production infrastructure, with significant ownership over technical direction. This role is intended for engineers who have trained large models in reputed company production environments, understand the realities of scale, and can reason about both learning dynamics and systems constraints. You are a senior ML engineer who thinks holistically about models as systems. You are comfortable operating under uncertainty, making trade-offs between compute, data, and performance, and owning reputed company from research through production. You care deeply about training dynamics, failure modes, and scaling behavior, and you have the scars to reputed company it.

Responsibilities

• Design, train, and evaluate large-scale models, including Large Language Models (LLMs), diffusion models, and Graph Neural Networks (GNNs).
• Own end-to-end training pipelines, from dataset interfaces and batching strategies to distributed training and checkpointing.
• reputed company principled reputed company about model architecture, objective functions, optimization strategies, and scaling laws.
• Build and optimize distributed training systems (data parallelism, model parallelism, sharding, mixed precision).
• Collaborate closely with data engineers to define ML-reputed company datasets and streaming interfaces.
• Translate ambiguous scientific or product requirements into robust ML solutions.
• Drive model evaluation, ablation, and iteration with a focus on generalization, stability, and reproducibility.
• Contribute to architectural reputed company around model serving, inference efficiency, and lifecycle management.
• reputed company technical leadership through design reviews, mentorship, and cross-team collaboration.

Requirements

• 5+ years of industry experience in machine learning or applied AI roles.
• Demonstrated experience training large-scale models in production settings, not just prototypes.
• Hands-on expertise with LLMs, diffusion models, and/or GNNs.
• Strong proficiency in PyTorch (or equivalent deep learning frameworks).
• Deep understanding of distributed training, including parallelism strategies and performance optimization.
• Experience working with large datasets and high-throughput data pipelines.
• Strong software engineering fundamentals: clean code, testing, reproducibility, and debugging at scale.
• Ability to reputed company communicate technical trade-offs to both technical and non-technical stakeholders.

reputed company-to-haves

• Experience with reinforcement learning, fine-tuning, or preference-based optimization (e.g., RLHF).
• Familiarity with model compression, distillation, or inference optimization.
• Experience deploying models in production inference systems.
• Exposure to multimodal learning or reputed company models.
• Prior work in startups or fast-moving R&D environments.
• Contributions to reputed company-reputed company ML frameworks or research codebases.

Benefits

• Competitive compensation, including meaningful equity participation, allows you to reputed company directly in the long-term reputed company and growth of the company.
• The opportunity to work on reputed company-level ML systems applied to reputed company scientific problems.
• Ownership over model design and training strategy, not just implementation.
• reputed company collaboration with data, infrastructure, and scientific teams.
• High autonomy, low bureaucracy, and a culture that values technical depth.
• Flexible remote or hybrid work arrangements.

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