Mineral

Education Requirements:

• BA/BS

• Masters

• PhD or equivalent practical experience

Experience Requirements:

• 10+ years experience playing a key role in the successful development and launch of a deep tech product

• Experience building innovative technologies in cross-functional & cross-technology settings

• Both startup experience during various stages of the company’s growth (or lifecycle) while also having an understanding around the complexities and nuances of operating within a large corporation

• Comfort and experience with ambiguous, early-stage projects. Ability to create and grow ideas into viable businesses from the chaotic ground up

• Able to effectively communicate both technical and non-technical information to mixed audiences.

Responsibilities:

• Proposing, assessing, and synthesising new X moonshot investigations

• Develop and evolve project strategy, including offering definition / roadmap, team priorities, and go-to-market plans.

• Perform techno-economic analysis and feasibility analysis

• Craft moonshot proposals with technical experts

• Lead small technical teams composed of internal or external experts to further the state of the art and build proof of concepts

Education Requirements:

• Ph.D. or Masters Degree in Computer Science, Machine Learning, or a related field.

Experience Requirements:

• At least 3 years of industry experience in a software engineering role

• Experience in AI infrastructure: building scalable data pipelines, inference servers, monitoring services, and more

• Deep understanding of ML pipelines, algorithms, and best practices for model development, deployment, and monitoring

• Strong background in deep learning concepts, architectures (e.g., Transformers, LLMs, GANs, etc.), and training techniques (e.g., Fine-tuning, self-supervised learning, transfer learning, etc.)

• Understanding of cloud platforms like GCP and infrastructure-as-code tools like Terraform and MLOps platforms like Kubeflow.

Responsibilities:

• Build and scale robust AI/ML cloud infrastructure for training, validation, and inference

• Create a smooth, efficient MLOps flow for staging, deploying and monitoring AI pipelines

• Create data pipelines, clean data, and generate synthetic datasets

• Train and validate models, using modern approaches including supervised fine tuning, retrieval augmented generation, parameter efficient fine-tuning, and more

• Actively collaborate with cross-functional teams, domain experts, and end users to refine product needs and ensure impactful technology solutions.

Education Requirements:

• Ph.D. or Masters Degree in Computer Science, Machine Learning, or a related field or equivalent applied research experience.

Experience Requirements:

• At least 3 years of industry experience in a software engineering role

• Deep understanding of ML pipelines, algorithms, and best practices for model development, deployment, and monitoring

• Strong background in deep learning concepts, architectures (e.g., Transformers, LLMs, GANs, etc.), and training techniques (e.g., Fine-tuning, self-supervised learning, transfer learning, etc.).

• Expertise and hands on experience with generative AI approaches: SFT, RAG, DPO, RLHF, etc.

• Experience in AI infrastructure: building scalable data pipelines, inference servers, monitoring services, and more

Responsibilities:

• Develop cutting edge generative AI approaches for challenging 3D data types

• Create data pipelines, clean data, and generate synthetic datasets

• Train and validate models, using modern approaches including supervised fine tuning, retrieval augmented generation, parameter efficient fine-tuning, and more

• Deploy, monitor and iterate!

• Actively collaborate with cross-functional teams, domain experts, and end users to refine product needs and ensure impactful technology solutions.