Senior AI/ML Engineer

This is a fully remote position, open to applicants in California, +4 more states.

📋 Description

• Design, train, and assess large-scale models, such as LLMs, diffusion models, and GNNs.

• Take responsibility for complete training pipelines from dataset interfaces and batching strategies to distributed training and checkpointing.

• Make informed decisions regarding model architecture, objective functions, optimization techniques, and scaling laws.

• Construct and enhance distributed training systems utilizing data parallelism, model parallelism, sharding, and mixed precision.

• Collaborate with data engineers to establish ML-ready datasets and streaming interfaces.

• Convert ambiguous scientific or product requirements into effective ML solutions.

• Lead model evaluation, ablation, and iteration with a focus on generalization, stability, and reproducibility.

• Contribute to model serving, inference efficiency, and lifecycle management architecture.

• Provide technical guidance through design reviews, mentorship, and collaboration across teams.

• Spearhead the design, training, and deployment of machine learning models that underpin Absentia Labs' AI capabilities.

⛳️ Requirements

• Over 5 years of industry experience in machine learning or applied AI roles.

• Proven experience in training large-scale models within production environments.

• Practical expertise with LLMs, diffusion models, and/or GNNs.

• Strong proficiency in PyTorch or similar deep learning frameworks.

• Comprehensive understanding of distributed training, including parallelism strategies and performance optimization.

• Experience with large datasets and high-throughput data pipelines.

• Solid software engineering principles, encompassing clean code, testing, reproducibility, and debugging at scale.

• Ability to articulate technical trade-offs to both technical and non-technical stakeholders.

• Experience in reinforcement learning, fine-tuning, or preference-based optimization is a plus.

• Familiarity with model compression, distillation, or inference optimization is advantageous.

• Experience in deploying models within production inference systems is a plus.

• Exposure to multimodal learning or foundation models is beneficial.

• Previous experience in a startup or fast-paced R&D environment is a plus.

• Contributions to open-source ML frameworks or research codebases are welcomed.

• An undergraduate degree in computer science is requested in the application form.

• Must be legally authorized to work in the U.S.

• Must indicate whether visa sponsorship will be necessary.

• A resume is required; a brief note describing experience in training large-scale models is also requested.

🏝️ Benefits

• Meaningful equity participation.

• Opportunity for bonuses.

• Flexible remote or hybrid work arrangements.

• High levels of autonomy with minimal bureaucracy.

• Chance to work on foundational ML systems that address real scientific challenges.

• Ownership of model design and training strategies.

• Close collaboration with data, infrastructure, and scientific teams.