Data Scientist Jobs

About The Position

Requirements

• Applied ML experience with hands-on fine-tuning of language models. You have prepared datasets, run training loops, evaluated results, and shipped a fine-tuned model. Familiarity with SFT, DPO, RLHF, or similar techniques.
• Understanding of evaluation methodology: how to design evals, interpret training graphs, and tell whether a model is actually better or just overfitting to the benchmark.
• Comfort with training infrastructure: GPUs, compute management, debugging common training failures. You don't need to be an infra engineer, but you should not be afraid of a stack trace from a training loop.
• Strong software engineering fundamentals (Python). You write clean, reproducible code. Experience with data pipelines and version control for datasets and experiments.
• 3+ years of engineering experience with meaningful exposure to applied ML or ML engineering (e.g., MLE, Applied Scientist, Data Scientist who shipped models to production, or ML-focused SWE).
• Demonstrated ability and interest to work in customer-facing environments, understanding user needs and translating domain requirements into training strategies.
• Self-starter with high agency and ownership, excelling in fast-paced startup environments where playbooks are still being written.

Responsibilities

• Fine-tune Reflection's open-weight models for customer-specific use cases: prepare datasets, configure training runs (SFT, preference optimization, reinforcement fine-tuning), and iterate based on evals.
• Build and maintain evaluation infrastructure: design eval suites, curate test sets, establish baselines, and measure whether fine-tuned models actually improve on the tasks customers care about.
• Prepare training data from raw customer inputs: inspect data quality, clean and format datasets, identify adversarial or noisy samples, and build reproducible data pipelines.
• Debug and diagnose training and inference issues: interpret loss curves, catch data quality problems, and identify when training dynamics indicate something is wrong.
• Support end-to-end deployments of fine-tuned models across hybrid environments (public cloud, VPC, and on-premises), helping ensure inference performance and reliability in production.
• Contribute to evolving playbooks, evaluation benchmarks, and best practices as part of a growing fine-tuning and evals practice.

Benefits

• Top-tier compensation: Salary and equity structured to recognize and retain the best talent globally.
• Comprehensive medical, dental, vision, life, and disability insurance.
• Fully paid parental leave for all new parents, including adoptive and surrogate journeys.
• Financial support for family planning.
• Paid time off when you need it
• Relocation support
• Lunch and dinner are provided daily.
• Regular off-sites and team celebrations.