AI Engineer (Middle/Senior)

General Information

As a Senior AI Engineer, you are a seasoned professional who leverages advanced knowledge of AI, machine learning, and deep learning to design, implement, and lead complex AI models and systems. You possess strong programming skills, a deep understanding of complex algorithms, and often steer the team's research and practical applications, acting as a technical leader and innovator. You will take on a larger role in resolving ambiguity, tackling especially thorny problems, and fostering broader collaboration across teams in a fast-paced, innovation-driven environment.

Key Responsibilities

Work closely as a project team to lead in the entire lifecycle of data science projects, from gathering business requirements to solution delivery, and deliver project outcomes to internal stakeholders or external customers.

Leads discussions with business stakeholders to frame problems, propose solutions, and ensure technical delivery meets business objectives.

Leads planning for end-to-end solution delivery within a project, defines workstream tasks for the team, aligns timelines with business priorities, and represents the DS track in PI planning and client milestone reviews.

Owns full solution design and delivery for a project or major workstream (e.g., model design, deployment strategy, MLOps considerations). Reviews junior work, manages delivery quality, presents to clients, ensures solution alignment with business goals.

Actively mentors junior and middle AI Engineers, conducts performance feedback, identifies growth areas, and contributes to technical capability uplift across the team or practice.

Proactive in leading and contributing to the growth of internal capabilities (e.g., training, knowledge-sharing, framework design).

Leading the exploration and application of Large Language Models (LLMs) and Generative AI, venturing into new research areas and setting the team's strategic direction.

Translating the latest research findings into high-performing systems and models that can be practically applied to enhance user experiences.

Actively engaging in all aspects of model development, from initial conceptualization and experimentation to final deployment in production environments.

Performing scaling law analyses on model size, data size, data mixture, and training compute to optimize AI models, particularly for large-scale datasets (e.g., self-driving data at Tesla).

Developing and implementing novel architectures and algorithms to effectively scale large End-to-End (E2E) self-driving models.

Creating and maintaining robust infrastructure for efficient, large-scale distributed training of E2E models, actively resolving compute and memory bottlenecks.

Evaluating and enhancing model performance, with a focus on increasing key metrics relevant to the product (e.g., miles driven without human intervention for autonomous systems).

Driving the implementation of best practices and monitoring systems to proactively detect and address issues in production environments, ensuring system reliability and performance.

Mentoring more junior team members, providing technical guidance, and contributing to internal knowledge sharing initiatives.

Required Skills & Qualifications

An advanced degree (Master's or Ph.D.) in Computer Science, Computer Engineering, Artificial Intelligence, Data Science, or a closely related field is generally required.

At least 3-5+ years experience shipping AI products at scale across multiple industries (healthcare, fintech, autonomous systems).

Extensive experience with MLOps tools and practices (e.g., model packaging, CI/CD for ML, model monitoring, ML pipelines).

In-depth experience in Machine Learning, with a particular emphasis on Large Language Models (LLMs) and Generative AI. A deep understanding of deep learning fundamentals, including optimization techniques, loss functions, and neural network architectures, is crucial.

Strong expertise in distributed computing and parallel processing techniques is vital for handling large-scale AI workloads. Experience with training large models on distributed computing environments is also expected.

A proven track record of innovations and executions in deep learning, demonstrated through shipping products or first-author publications at leading AI conferences, is a strong differentiator. For research-focused roles, a solid publication record in top-tier venues is often a requirement.

Proven problem-solving and leadership abilities, with the capacity to steer the team's research and practical applications in a collaborative and fast-paced environment.

Excellent independent problem-solving skills and strong communication skills are also essential.

Familiarity with agile delivery practices.