Role OverviewThe role blends scientific rigor with practical software engineering, requiring deep understanding of modern ML and deep learning techniques alongside the ability to build robust, scalable, and well-instrumented production pipelines.
What You Will Do
Design, prototype, and evaluate applied AI solutions across natural language, vision, recommendation, and structured data domains.
Why It Might Be a Fit
The ideal candidate stays current with the rapidly evolving AI research landscape, can critically evaluate new techniques for real-world applicability, and is comfortable operating across the full lifecycle from problem framing and experimentation to deployment and continuous improvement.
Requirements
- Master’s or PhD in Computer Science, Machine Learning, Statistics, or a closely related field; or equivalent applied experience.
- 12 or more years of combined research and applied ML engineering experience.
- Strong proficiency in Python and modern ML frameworks such as PyTorch or JAX.
- Hands-on experience training, fine-tuning, and evaluating deep learning models at non-trivial scale.
- Solid grounding in mathematics, statistics, and the theoretical foundations of modern ML.
- Experience taking ML models from research prototype to production with appropriate observability and safeguards.
- Familiarity with distributed training, mixed-precision training, and accelerator hardware.
- Strong written and verbal communication skills, including ability to explain complex methods clearly.
- Demonstrated ability to read, evaluate, and adapt techniques from current research literature.
- Track record of shipping impactful applied AI projects.
Benefits
- Salary Range: $100,000–$150,000 Annually
- Benefits not explicitly mentioned in the job posting.
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