NewsBreak

Technology

MachineLearningEngineer,LLMPost-Training

$150–230k Mountain View, California, United States FULL TIME
Market Sentiment
HIGH DEMAND

Neural analysis suggests this role is
optimal for Mid+ candidates.

The Brief

“Machine Learning Engineer, LLM Post-Training at NewsBreak. Skills: LLM Post-Training, Reinforcement Learning, Data Engineering, Large-scale GPU training. Lead post-training of LLMs. Design data for training stages”

What You'll Achieve.

Ship model improvements quickly; Deliver targeted model capabilities

Industry & Context.

Technology
Problems you'll solve

Solving meaningful challenges

What They're Looking For.

Must Have

Hands-on LLM post-training experience, Demonstrated practical RL experience, Independently design data-preparation plans, Trained LLMs on mid-to-large GPU hardware, Comfortable with distributed training, PyTorch working familiarity, Solid understanding of tokenization, Solid understanding of attention, Solid understanding of chat templates, Solid understanding of common failure modes

Nice to Have

Experience designing reward models, Experience designing rule-based verifiers, Experience with tool-use training, Experience with agentic model training, Publications in LLM post-training, Publications in RL, Open-source contributions in LLM post-training, Open-source contributions in RL

What You'll Do.

Lead post-training of LLMs

Design data for training stages

Build data for training stages

Curate data for training stages

Define data-preparation strategies

Partner with business stakeholders

Partner with product stakeholders

Understand business scenarios

Convert requirements into training plans

Deliver targeted model capabilities

Run large-scale training

Apply distributed-training techniques

Build evaluation pipelines

Build reward pipelines

Build verifier pipelines

Measure model quality

Prevent model regressions

Ensure training–serving consistency

Stay current with research

Turn techniques into code

How You'll Work.

Team & Collaboration

Work with product teams; Work with business teams; Work across research teams; Work across product teams

Communication Scope

Communication skills

Process & Methodology

Fast iteration

Full Job Description

About NewsBreak Founded in 2015, NewsBreak is the 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. Recognized by Fast Company as #32 on the Top Workplaces for Innovators, we're proud to be Great Place to Work® certified and home to a dynamic team of technologists, product innovators, and business leaders who are passionate about solving meaningful challenges at scale. Together, we reached unicorn status in 2021, and we remain committed to continuing this high-growth trajectory with the right team to fulfill our mission: building the infrastructure layer for content intelligence. If you’re inspired to dream big, innovate fast, and make a difference, we’d love to hear from you! For more information, visit www.newsbreak.com/about 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. Just as important, you will work directly with product and business teams to translate real-world use cases into concrete training objectives and ship model improvements quickly. This is a high-ownership role for someone who has actually trained models, not just read about it. 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 tailor

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