AI Research Engineer (Applied AI)
New
100% Remote (Continental United States)Full-TimeSenior
Salary100,000 - 150,000 USD per year
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Job Details
- Experience
- 6+ years
- Required Skills
- PythonMachine LearningPyTorchDeep LearningNLPComputer Vision
Requirements
- Master’s or PhD in Computer Science, Machine Learning, Statistics, or a closely related field; or equivalent applied experience.
- Six 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.
Responsibilities
- Design, prototype, and evaluate applied AI solutions across natural language, vision, recommendation, and structured data domains.
- Translate ambiguous business problems into well-scoped ML formulations with clear success metrics and evaluation strategies.
- Implement rigorous experimentation workflows including baselines, ablations, and statistically sound evaluation methodology.
- Build production-quality training and inference pipelines using modern ML frameworks and orchestration tools.
- Optimize models for accuracy, latency, throughput, and cost based on production requirements.
- Develop tooling for dataset construction, labeling, validation, and ongoing monitoring of data quality.
- Partner with product, design, and domain experts to ensure model behavior aligns with user needs and policy requirements.
- Implement safety, fairness, and reliability evaluations and incorporate findings into model selection decisions.
- Document research findings, design decisions, and operational characteristics clearly for both technical and non-technical audiences.
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