Machine Learning Engineer, LLM Post-Training

NewsBreak; Mountain View (CA); In loco; USD 130.000 - 160.000; Tempo pieno

Vantaggi offerti da questo lavoro

Health, dental, and vision care

401(k) plan with company matching

Paid time off and holidays

Descrizione del lavoro

NewsBreak in Mountain View, California, is seeking a Machine Learning Engineer to lead the post-training of large language models. You will drive continuous pre-training, supervised fine-tuning, and reinforcement learning while collaborating closely with product and business teams.

The ideal candidate has hands-on experience with RL techniques and strong data engineering skills, particularly with GPU training. This role offers competitive benefits such as health coverage and a top-tier 401(k) plan.

Competenze

• Hands-on experience with continuous pre-training, supervised fine-tuning, and reinforcement learning.
• Ability to design data preparation plans for business scenarios.
• Experience with distributed training on mid-to-large GPU hardware.

Mansioni

• Lead post-training of LLMs across the full pipeline.
• Design and build data that drives each training stage.
• Partner with stakeholders to understand business requirements.

Conoscenze

Hands-on LLM post-training experience

Strong data engineering for ML

Proven large-scale GPU training ability

Strong PyTorch fundamentals

Understanding of tokenization and attention

Fast iteration and business impact

Strumenti

PyTorch

Hugging Face TRL/Accelerate

DeepSpeed

Descrizione del lavoro

Machine Learning Engineer, LLM Post-Training

Mountain View, California, United States

About NewsBreak

Founded in 2015, NewsBreak is a Content Intelligence platform shaping the future content economy. With over 40 million monthly active users, our flagship platform delivers highly personalized local news and information powered by advanced AI, recommendation systems, and adtech.

About the Role

We are looking for a hands‑on Machine Learning Engineer to drive the post‑training of our large language models, with a strong emphasis on reinforcement learning (RL). You will own the full post‑training stack — continuous pre‑training (CPT), supervised fine‑tuning (SFT), and RL — along with the data preparation that powers it. You will work directly with product and business teams to translate real‑world use cases into concrete training objectives and ship model improvements quickly.

Responsibilities

• Lead post‑training of our LLMs across the full pipeline: continuous pre‑training, SFT, and reinforcement learning, with RL as the primary focus (e.g., RLHF, PPO, GRPO, DPO, and related methods).
• Design, build, and curate the data that drives each training stage — instruction/SFT datasets, preference pairs, reward signals, on‑policy rollouts, and rejection‑sampled completions — and define data‑preparation strategies tailored to specific business needs.
• Partner closely with business and product stakeholders to understand their scenarios, rapidly convert requirements into training plans, and deliver targeted model capabilities on tight timelines.
• Run large‑scale training on mid‑to‑large GPU clusters, applying distributed‑training techniques (data parallelism, FSDP, and where relevant tensor/pipeline parallelism) and tuning for throughput and stability.
• Build and maintain evaluation and reward/verifier pipelines to measure model quality, prevent regressions, and ensure training‑serving consistency.
• Stay current with post‑training research and turn promising techniques into working, production‑ready code.

Requirements

• Hands‑on LLM post‑training experience. You have personally run CPT, SFT, and RL training — with demonstrated, practical RL experience (RLHF / PPO / GRPO / DPO or similar), beyond just launching training scripts.
• Strong data engineering for ML. You can independently design data‑preparation plans for a given business scenario — sourcing, cleaning, filtering, labeling strategy, and synthetic/preference data generation — to meet specific product requirements.
• Proven large‑scale GPU training ability. You have trained LLMs on mid‑to‑large GPU hardware and are comfortable with distributed training and debugging at scale.
• Strong PyTorch fundamentals; working familiarity with frameworks such as Hugging Face TRL/Accelerate, DeepSpeed or FSDP, and inference engines like vLLM.
• Solid understanding of tokenization, attention, chat templates, and common failure modes in alignment/agent training.
• A bias toward fast iteration and business impact, with strong communication skills to work across research and product teams.

Preferred Qualifications

• Experience designing reward models or rule‑based verifiers for RL.
• Experience with tool‑use / agentic model training (function calling, multi‑step planning).
• Publications or open‑source contributions in LLM post‑training or RL.

Benefits

• Health, dental, and vision care for you and your family (100% coverage for employee).
• Top‑tier 401(k) plan with company matching.
• Paid time off and paid holidays.
• FSA, HSA and commuter benefits programs.
• Team activity budget.

Equal Employment Opportunity

As set forth in NewsBreak’s Equal Employment Opportunity policy, we do not discriminate on the basis of any protected group status under any applicable law.