About NewsBreakFounded 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 ;br>
About the RoleWe 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 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.
BenefitsWe offer a competitive benefits package:
- 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
The US base salary range for this full-time position is listed below. Pay may vary based on a number of factors including job-related skills, level, experience, geographic location and relevant education or training. At NewsBreak, we design our overall rewards package to attract top talents. Depending on the position, the role may also be eligible for discretionary bonus and options. Your recruiter can share more details during the hiring process.
Annual Base Pay Range
$150,000-$230,000 USD
CPRA Privacy Notice for California Candidates
Never miss an opportunity! Create an alert based on the job you applied for.