AI Research Engineer – Applied AI

Location

United States

Posted

9 days ago

Salary

0

Seniority

Senior

Postgraduate Degree6 yrs expEnglishPythonPyTorch

Job Description

AI Research Engineer – Applied AI

Bright Vision Technologies

• 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. • Stay current with the latest research in deep learning, large language models, and adjacent areas, and assess applicability to internal use cases. • 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. • Collaborate with ML platform engineers to ensure efficient use of compute, storage, and accelerator resources. • 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. • Mentor engineers on applied ML methodology, evaluation rigor, and responsible deployment. • Contribute to internal knowledge sharing, reading groups, and prototype-to-production playbooks. • Influence the broader AI roadmap based on research insight, capability gaps, and emerging opportunities.

Job 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.

Benefits

  • Comprehensive benefits
  • Competitive compensation packages
  • Supportive work-life balance

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