AI Research Engineer
New
B
Bright Vision TechnologiesArtificial intelligence
100% Remote (U.S.)Full-TimeSenior
Salary100,000 - 150,000 USD per year
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Job Details
- Experience
- Ten or more years of combined research and applied ML engineering experience.
- Required Skills
- PythonMachine LearningPyTorchDeep LearningLLM
Requirements
- Master’s or PhD in Computer Science, Machine Learning, Statistics, or a closely related field, or equivalent applied experience.
- Ten 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.
- 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.
- Ability to read, evaluate, and adapt techniques from current research literature.
- Preferred: published research at top-tier AI/ML venues.
- Preferred: experience with large language model training, fine-tuning, or evaluation.
- Preferred: familiarity with retrieval-augmented generation, agentic systems, or multimodal architectures.
- Preferred: exposure to responsible AI, model evaluation, and alignment practices.
Responsibilities
- Design, prototype, and evaluate applied AI solutions across natural language, vision, recommendation, and structured data domains.
- Translate business problems into well-scoped ML formulations with success metrics and evaluation strategies.
- Evaluate current deep learning and large language model research for applicability to internal use cases.
- Implement experimentation workflows with baselines, ablations, and statistically sound evaluation.
- Build production-quality training and inference pipelines using modern ML frameworks and orchestration tools.
- Collaborate with ML platform engineers on compute, storage, and accelerator resources.
- Optimize models for accuracy, latency, throughput, and cost.
- Develop tooling for dataset construction, labeling, validation, and data quality monitoring.
- Implement safety, fairness, and reliability evaluations and document research findings and design decisions.
- Mentor engineers on applied ML methodology, evaluation, and responsible deployment.
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