Forward Deployed Engineer - LLM Post-training

About the role

The Applied AI team at Our Mission Reflection drives model fine-tuning and evaluations for enterprise customers. This team adapts Reflection's open-weight models for specific customer domains, tasks, and constraints.

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).
• 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

• Master's degree in Computer Science, Applied Mathematics, Statistics, or related field.
• Experience with large-scale machine learning systems and distributed computing.
• Knowledge of natural language processing and deep learning frameworks (e.g., TensorFlow, PyTorch).
• Experience with cloud platforms (AWS, Google Cloud, Azure).
• Excellent communication skills and ability to work effectively with cross-functional teams.

Skills

• Hands-on experience with model fine-tuning and evaluation.
• Proficiency in Python and other relevant programming languages.
• Experience with data preprocessing and cleaning.
• Knowledge of cloud-based infrastructure and deployment practices.
• Ability to work independently and manage multiple projects simultaneously.

Benefits

• 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.

Pay

Competitive salary and equity structure.

Schedule

Full-time position.