$80K - $110K/yr
Develop robust evaluation strategies, including evaluation datasets, offline and online testing ... Experience with fine-tuning or preference optimization techniques such as RLHF or DPO is a plus.
$80K - $110K/yr
Develop robust evaluation strategies, including evaluation datasets, offline and online testing ... Experience with fine-tuning or preference optimization techniques such as RLHF or DPO is a plus.
Online Rlhf information
What is an online RLHF?
Online RLHF (Reinforcement Learning from Human Feedback) jobs typically involve helping to train AI models by providing human feedback on their outputs. Workers in these roles might review model responses, rate the quality of generated text, or suggest improvements to help the AI learn to produce better results. These jobs are often remote and can be done part-time or as contract work. They play a crucial role in improving the safety, usefulness, and accuracy of AI systems by aligning them more closely with human preferences.
What are some common challenges faced by online RLHF specialists when collaborating with cross-functional teams?
Online RLHF specialists often work closely with machine learning engineers, data annotators, and product managers. A common challenge is ensuring that feedback from human annotators is accurately integrated into model training, which requires clear communication and well-defined annotation guidelines. Additionally, balancing the pace of model updates with the need for high-quality human feedback can be demanding. Effective collaboration and regular syncs are essential to maintain alignment and achieve project goals.
What are the key skills and qualifications needed to thrive as an online RLHF specialist, and why are they important?
To thrive as an Online RLHF Specialist, you need a strong background in machine learning, reinforcement learning, and data analysis, typically supported by a degree in computer science or a related field. Familiarity with technical tools like Python, PyTorch or TensorFlow, and experience with human feedback systems or annotation platforms are highly valuable. Strong problem-solving, attention to detail, and the ability to communicate complex concepts clearly are crucial soft skills. These qualifications ensure the effective training and evaluation of AI models, leading to more accurate and reliable machine learning systems.
What is the difference between Online Rlhf vs Online Rlhf?
| Aspect | Online Rlhf | Online Rlhf |
|---|---|---|
| Credentials | Typically requires certification in online health coaching or related fields | Typically requires certification in online health coaching or related fields |
| Work Environment | Remote, online platform-based | Remote, online platform-based |
| Industry Usage | Common in health and wellness sectors | Common in health and wellness sectors |
| Job Focus | Providing health guidance and support online | Providing health guidance and support online |
Online Rlhf and Online Rlhf are the same role, often used interchangeably. Both involve providing health and wellness support remotely, requiring similar certifications and working within the online health industry. The key difference is often in terminology rather than job function.
What are the most commonly searched types of Rlhf jobs in Missouri?
The most popular types of Rlhf jobs in Missouri are:
What are popular job titles related to Online Rlhf jobs in Missouri?
For Online Rlhf jobs in Missouri, the most frequently searched job titles are:
What job categories do people searching Online Rlhf jobs in Missouri look for?
The top searched job categories for Online Rlhf jobs in Missouri are:
Senior ML Engineer
On-site
Jobgether
$80K - $110K/yr
Full-time
Posted 7 days ago
Job description
This position is listed on behalf of a partner company, who manages all applications and next steps. Our partner is looking for a Senior ML Engineer based in Netherlands.
This role offers the opportunity to build production-grade AI systems in a high-impact healthcare environment where reliability, evaluation, and safety are essential. You'll own machine learning initiatives end-to-end, taking complex problems from early exploration through deployment and continuous improvement. A major focus will be designing agentic LLM systems with tool use, retrieval, orchestration, and robust evaluation frameworks. You'll work across the AI stack, from data preparation and model adaptation to serving, monitoring, and production feedback loops. Close collaboration with Product, Clinical, and Engineering teams will be critical to translating real-world healthcare needs into effective technical solutions. You'll also contribute to engineering excellence through mentorship, knowledge sharing, and practical adoption of AI across the team.
Accountabilities
- Own machine learning projects end-to-end, from problem exploration and experimentation through production deployment, monitoring, and ongoing iteration.
- Design and build reliable agentic LLM systems using multi-step workflows, tool calling, retrieval, and orchestration techniques suitable for high-stakes clinical environments.
- Develop robust evaluation strategies, including evaluation datasets, offline and online testing harnesses, LLM-as-judge pipelines with human review, and regression testing.
- Improve model performance using evidence-driven approaches such as prompt engineering, retrieval optimization, distillation, fine-tuning, or other appropriate techniques.
- Work across the full machine learning lifecycle, including data preparation, model adaptation, serving, monitoring, and production feedback loops.
- Partner with Product, Clinical, and Engineering stakeholders to translate clinical requirements into technical solutions and identify trade-offs early.
- Review code, share technical knowledge, contribute to engineering best practices, and mentor less experienced engineers.
- Continuously explore and apply AI-assisted approaches that improve individual productivity, team workflows, products, and engineering processes.
- Proven experience shipping production machine learning systems that are relied upon by real users or customers.
- Hands-on experience developing and deploying LLM-based systems, including prompting, retrieval, tool calling, and agent-style workflows.
- Strong evaluation expertise, with experience building evaluation datasets, frameworks, testing methodologies, and mechanisms for distinguishing meaningful improvements from statistical or operational noise.
- Strong foundations in machine learning and the ability to select appropriate approaches based on technical requirements, evidence, and trade-offs.
- Experience working with ambiguous or loosely defined problems and transforming them into reliable production solutions.
- Strong software engineering skills, including writing production-quality code, working with distributed systems, and debugging complex machine learning pipelines.
- Clear communication skills and the ability to collaborate effectively with both technical and clinical stakeholders.
- Demonstrated AI fluency, with at least Level 1 proficiency: using AI regularly to improve personal productivity. More senior expectations may include building AI-enabled workflows or embedding AI into products and processes.
- Experience with fine-tuning or preference optimization techniques such as RLHF or DPO is a plus.
- Experience in healthcare AI or other high-stakes domains where system errors can have significant consequences is advantageous.
- Experience building agent frameworks or evaluation tooling from scratch is a plus.
- Open-source contributions, technical writing, or other forms of technical knowledge sharing are valued.
- Willingness to work within a distributed European environment, with the position open to candidates across Europe.
- Competitive compensation adjusted according to the local market and cost of living in the European country where you are hired.
- Total compensation may include base salary, variable compensation, bonuses or incentives, and equity where applicable.
- Compensation is reviewed based on skills, qualifications, experience, location, market conditions, and demonstrated impact.
- Opportunity for compensation growth as your responsibilities and contribution increase.
- Country-specific benefits and perks aligned with local regulations and market practices.
- Opportunity to work on high-impact AI systems addressing complex healthcare challenges.
- Exposure to cutting-edge LLMs, agentic AI, evaluation frameworks, and production machine learning at significant scale.
- Collaborative environment spanning AI, engineering, product, and clinical expertise.
- Opportunity to mentor other engineers and contribute to technical practices and knowledge sharing.
- European-wide hiring flexibility, with employment arrangements and benefits adapted to the country of hire.
How Jobgether works:
We use an AI-powered matching process to ensure your application is reviewed quickly, objectively, and fairly against the role's core requirements. Our system identifies the top-fitting candidates, and this shortlist is then shared directly with the hiring company. The final decision and next steps (interviews, assessments) are managed by their internal team.
We appreciate your interest and wish you the best!
Why Apply Through Jobgether?
Data Privacy Notice: By submitting your application, you acknowledge that Jobgether will process your personal data to evaluate your candidacy and share relevant information with the hiring employer. This processing is based on legitimate interest and pre-contractual measures under applicable data protection laws (including GDPR). You may exercise your rights (access, rectification, erasure, objection) at any time.
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We may use artificial intelligence (AI) tools to support parts of the hiring process, such as reviewing applications, analyzing resumes, or assessing responses and identifying potential inconsistencies or verification signals in application materials based on available information. These tools assist our recruitment team but do not replace human judgment. Final hiring decisions are ultimately made by humans. If you would like more information about how your data is processed, please contact us.
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