Reflection AI

3 months ago

Forward Deployed Engineer - LLM Post-training

✓ Verified Job

Canonical indexed version, validated from employer's careers page.

Source: Company careers pageValidated by: Fursa AI

Last checked: 12 hours agoStatus: Live

Job Summary

Role as a core member of the Applied AI team responsible for fine-tuning open-weight language models for enterprise use, building evaluation harnesses, and deploying adapted models to production. You will work with customer data, design training runs (SFT, preference optimization, RLHF/DPO), and collaborate with research and customer teams to ensure measurable improvements and robust deployments.

Location: San Francisco, New York

Workplace: Onsite

Employment Type: Full time

Job Function: Data Science & Machine Learning

Key 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.

Pay and Benefits

Perks:Health InsuranceDentalVisionLife InsuranceParental LeaveRelocationEquity

Key Requirements

•Applied ML experience with hands-on fine-tuning of language models, including preparing datasets, running training loops, evaluating results, and shipping a fine-tuned model; familiarity with SFT, DPO, RLHF, or similar techniques.
•Understanding of evaluation methodology: how to design evals, interpret training graphs, and assess true model improvement versus overfitting.
•Comfort with training infrastructure: GPUs, compute management, debugging common training failures; not afraid of stack traces from training loops.
•Strong software engineering fundamentals (Python); ability to 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, translating domain requirements into training strategies.
•Self-starter with high agency and ownership, thriving in fast-paced startup environments.

Experience:3+ yearsApplied MLOpen weight modelsEnterprise AI

Skills:CommunicationProblem-solvingOwnership

Tech Stack:PythonGPUsSFTDPORLHFTraining pipelinesEvaluation harnesses

Company Brief

Reflection AI

Builds frontier autonomous AI systems focused on autonomous coding agents (product: Asimov) to create organizational superintelligence, founded by former DeepMind/Google researchers and hiring across SF, NYC, London.

Industry: AI & Machine Learning

Company Size: Small (11 to 50 employees)

Growth: Growth Stage Startup

Funding: Series A

Headquarters: San Francisco, United States

Founded: 2024

Website LinkedIn Glassdoor

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Reflection AI

3 months ago

Forward Deployed Engineer - LLM Post-training

✓ Verified Job

Canonical indexed version, validated from employer's careers page.

Source: Company careers pageValidated by: Fursa AI

Last checked: 12 hours agoStatus: Live

Job Summary

Location: San Francisco, New York

Workplace: Onsite

Employment Type: Full time

Job Function: Data Science & Machine Learning

Key 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.

Pay and Benefits

Perks:Health InsuranceDentalVisionLife InsuranceParental LeaveRelocationEquity

Key Requirements

•Applied ML experience with hands-on fine-tuning of language models, including preparing datasets, running training loops, evaluating results, and shipping a fine-tuned model; familiarity with SFT, DPO, RLHF, or similar techniques.
•Understanding of evaluation methodology: how to design evals, interpret training graphs, and assess true model improvement versus overfitting.
•Comfort with training infrastructure: GPUs, compute management, debugging common training failures; not afraid of stack traces from training loops.
•Strong software engineering fundamentals (Python); ability to 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, translating domain requirements into training strategies.
•Self-starter with high agency and ownership, thriving in fast-paced startup environments.

Experience:3+ yearsApplied MLOpen weight modelsEnterprise AI

Skills:CommunicationProblem-solvingOwnership

Tech Stack:PythonGPUsSFTDPORLHFTraining pipelinesEvaluation harnesses

Company Brief

Reflection AI

Industry: AI & Machine Learning

Company Size: Small (11 to 50 employees)

Growth: Growth Stage Startup

Funding: Series A

Headquarters: San Francisco, United States

Founded: 2024

Website LinkedIn Glassdoor