AI Research Engineer
$100,000–$150,000 year
RemoteUnited States
Job Summary
Design applied AI solutions across natural language, vision, and structured data domains by translating ambiguous business problems into well-scoped ML formulations with clear success metrics. Implement rigorous experimentation workflows including baselines, ablations, and statistically sound evaluation methodology to assess applicability of new techniques. Build production-quality training and inference pipelines using modern ML frameworks, optimizing models for accuracy, latency, throughput, and cost while collaborating with platform engineers on compute and storage resources. Develop tooling for dataset construction, labeling, and ongoing monitoring, then partner with product and domain experts to align model behavior with user needs and policy requirements. Document research findings and mentor engineers on applied ML methodology and responsible deployment practices.
Required Qualifications
- 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
Desired Qualifications
- Published research at top-tier AI/ML venues
- Experience with large language model training, fine-tuning, or evaluation
- Familiarity with retrieval-augmented generation, agentic systems, or multimodal architectures
- Exposure to responsible AI, model evaluation, and alignment practices
- Experience contributing to open-source ML projects
Hiring someone like this?
Get your role in front of qualified candidates on Sorce.