Forward Deployed Engineer - LLM Post-training

About the role

The Applied AI team at Our Mission Reflection is dedicated to adapting open-weight models for specific customer domains, tasks, and constraints. This role involves fine-tuning models, building evaluation infrastructure, preparing training data, and supporting deployments.

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.

Requirements

• Applied ML experience with hands-on fine-tuning of language models.
• 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.
• Strong software engineering fundamentals (Python): 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.

Qualifications

Top-tier compensation: Salary and equity structured to recognize and retain our talent globally.

Stock options: Everyone who joins and contributes to Reflection's success gets to share in the upside through stock options.

Health & wellness: Comprehensive medical, dental, vision, and life, with an annual wellness allowance.

Meals: Lunch and dinner are provided in the office daily.

Life & family: 22 weeks paid parental leave for all new birthing and non-birthing parents, including adoptive and surrogate journeys.

Vacation days: Unlimited paid time off in the U.S. and 30 days in the U.K.

Sponsorship support: We sponsor visas to help exceptional talent join our team and support long-term immigration pathways where applicable.

Team building: We have regular off-sites, happy hours, and team celebrations.