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AI Engineer

About Us

Our mission is to make healthcare reimbursement fair and transparent, so providers can spend more time caring for patients and less time haggling over costs. We specifically focus on the most complex AI challenges that require novel R&D, building products that are fit for purpose in healthcare. We are backed by some of the top healthcare investors and growing fast. Join us!


The Role

As an AI Engineer on our team, you will architect and optimize the training and inference infrastructure that underpins our healthcare language models. You'll collaborate closely with research scientists, product teams, and end-users to ensure our AI solutions are robust, scalable, and deployable for real-world clinical applications. You will work with state-of-the-art open-source LLMs running on GPUs, helping us advance healthcare NLP in production environments.

We are looking for a candidate who can come into our NYC (Soho) office 3+ days per week.

Key Responsibilities

  • LLM Training & Inference Infrastructure:
    Develop and maintain GPU-accelerated systems for large-scale training and inference, ensuring high throughput and low latency. Optimize distributed training pipelines, handle multi-node clusters, and evaluate state-of-the-art frameworks for open-source language models.

  • Model Optimization & Deployment:
    Implement techniques such as model parallelism, quantization, knowledge distillation, and efficient serving to deliver cost-effective and fast inference for mission-critical healthcare applications.

  • Collaboration with AI Research:
    Work with our NLP research team to integrate new model architectures, fine-tuned weights, and evaluation benchmarks into production pipelines. Establish best practices for version control, reproducible experiments, and continuous model improvement.

  • Healthcare Data Integration:
    Collaborate with data engineering teams to ingest and preprocess large clinical datasets (EHR, claims data, etc.) in GPU-friendly formats. Help define secure and scalable data workflows to comply with healthcare regulations.

  • Monitoring & Scalability:
    Set up monitoring, logging, and alerting for AI systems in production, ensuring uptime and performance metrics are met. Implement strategies for autoscaling and distributed resource management.

  • Technical Leadership & R&D:
    Stay current with the latest research in large-scale machine learning, GPU acceleration, and MLOps. Champion best practices to the broader team, sharing insights through presentations, docs, and code reviews.


About You

Technical Expertise

  • Educational Background:
    MS/PhD in Computer Science, Electrical Engineering, or a related field (or equivalent industry experience).

  • Hands-on Experience:
    2+ years of building and optimizing ML infrastructure for large-scale training and inference. Familiarity with GPU-accelerated computing, distributed systems, and open-source LLMs.

  • Deep Knowledge of ML & MLOps:
    Proficiency in Python and frameworks like PyTorch for large-scale model training. Experience with containerization (Docker/Kubernetes), experiment tracking, CI/CD, and monitoring for AI systems.

  • Performance Tuning & Deployment:
    Track record of improving inference efficiency and throughput via techniques like model parallelism, quantization, or knowledge distillation.

  • Startup Mindset:
    Comfortable with ambiguity, rapid iteration, and owning projects end-to-end. Driven to deliver meaningful outcomes and iterate quickly on user feedback.

Benefits

  • Competitive Compensation:
    Top-of-market salary plus equity.

  • Flexible PTO:
    Generous vacation policy and a culture that supports work-life balance.

  • Team Culture:
    Collaborative environment with regular team-building events. Mission-driven work that makes a tangible impact in healthcare.

Hiring Process

  1. Initial Application:
    Submit your resume/LinkedIn and a brief statement about why you’re interested.

  2. Intro Call:
    Discuss your background, career goals, and our mission to see if there’s a mutual fit.

  3. Technical Interviews (2x):
    Includes a programming or system design exercise focused on large-scale training/inference and GPU workflows.

  4. Referees:
    Provide 2 references who can speak to your professional/technical accomplishments.

  5. Culture Interview:
    Explore ways of working, team fit, and give you a chance to ask questions.

  6. Offer
    We’ll extend a competitive offer for the right candidate to join our growing team.

Average salary estimate

$140000 / YEARLY (est.)
min
max
$120000K
$160000K

If an employer mentions a salary or salary range on their job, we display it as an "Employer Estimate". If a job has no salary data, Rise displays an estimate if available.

What You Should Know About AI Engineer, Phare Health

Are you ready to make a significant impact in the healthcare sector? We're on the lookout for an AI Engineer to join our dynamic team focused on revolutionizing healthcare reimbursement processes. As an AI Engineer, your primary responsibility will be to architect and optimize cutting-edge training and inference infrastructures that drive our healthcare language models. You'll collaborate with top-notch research scientists and product teams to ensure our AI solutions are not only robust but also scalable for real-world clinical applications. Your expertise in working with state-of-the-art open-source large language models (LLMs) running on GPUs will be crucial in enhancing healthcare natural language processing (NLP) in production settings. We're a fast-growing company based in NYC (Soho), and we believe that great ideas come from diverse teams. If you're passionate about pushing boundaries in AI and healthcare, we want to hear from you! Join us in transforming how healthcare operates so providers can focus on what truly matters—caring for patients.

Frequently Asked Questions (FAQs) for AI Engineer Role at Phare Health
What are the responsibilities of an AI Engineer at this company?

As an AI Engineer at our company, you will be responsible for developing and maintaining GPU-accelerated systems for large-scale training and inference. You'll optimize distributed training pipelines, work closely with the NLP research team to integrate new model architectures, and collaborate with data engineering teams to handle large clinical datasets. Additionally, you will set up monitoring, logging, and performance metrics for AI systems in production.

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What qualifications are needed to become an AI Engineer here?

To qualify as an AI Engineer at our company, you should have a Master's or Ph.D. in Computer Science, Electrical Engineering, or a related field, or equivalent industry experience. A minimum of 2 years of hands-on experience in building and optimizing ML infrastructure, a deep understanding of ML and MLOps, and proficiency in Python with frameworks like PyTorch are essential for success in this role.

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What is the work culture like for AI Engineers at this company?

The work culture for AI Engineers at our company is collaborative and mission-driven. We emphasize a strong team culture, flexible PTO, and regular team-building events. We value diverse perspectives and encourage innovation, enabling you to take ownership of your projects and drive meaningful outcomes in the healthcare domain.

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Is remote work an option for AI Engineers at this company?

While we are open to a flexible work arrangement, we do require AI Engineers to work from our NYC (Soho) office at least 3 days per week. This approach promotes collaboration and enhances the team dynamic, allowing for effective brainstorming and problem-solving sessions.

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How does the company support professional development for AI Engineers?

Our company is committed to the professional development of AI Engineers. We encourage continuous learning through exposure to the latest research in large-scale machine learning, hosting regular presentations and workshops, and providing opportunities for code reviews and feedback. This fosters an environment where your skills can continuously grow.

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Common Interview Questions for AI Engineer
How do you approach optimizing large-scale training and inference systems?

When optimizing large-scale training and inference systems, I focus on monitoring performance metrics, identifying bottlenecks, and implementing techniques such as model parallelism and quantization. I also evaluate the effectiveness of different frameworks to ensure the best results for production environments.

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Can you explain your experience with GPU-accelerated computing?

Throughout my career, I have utilized GPU-accelerated computing for various machine learning tasks such as model training and inference. I have experience in setting up multi-node clusters, leveraging libraries like PyTorch, and optimizing resource management to ensure efficient processing.

Join Rise to see the full answer
What techniques do you use for model deployment in production?

For model deployment in production, I implement techniques such as knowledge distillation and efficient serving strategies. I also ensure that robust CI/CD pipelines are in place to facilitate smooth integration and quick iterations based on user feedback.

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How do you ensure compliance with healthcare regulations while working with data?

To ensure compliance with healthcare regulations, I collaborate closely with data engineering teams to establish secure workflows for ingesting and preprocessing clinical datasets. I prioritize implementing data security measures, training algorithms on anonymized data, and staying updated with legal requirements.

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Describe how you would collaborate with research teams in your role.

Collaboration with research teams is vital in my role as an AI Engineer. I regularly engage in discussions about new model architectures and share insights from production performance to contribute to continuous improvement. This collaborative effort ensures that our models remain state-of-the-art and effectively address clinical needs.

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What strategies do you have for monitoring AI systems in production?

I set up comprehensive monitoring and alerting systems that track performance metrics, uptime, and potential anomalies. I also implement autoscaling strategies to manage resource allocation effectively, ensuring that AI systems maintain optimal performance under varying workloads.

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How would you handle ambiguity in a startup environment?

In a startup environment, I embrace ambiguity by maintaining a flexible mindset and focusing on problem-solving. I break down complex challenges into manageable tasks, iterate quickly based on feedback, and leverage collaboration to drive innovative solutions that align with company goals.

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What role does feedback play in your work as an AI Engineer?

Feedback plays a crucial role in my work as it guides me in refining models and improving processes. I actively seek out feedback from peers and stakeholders, which I incorporate into my projects to ensure alignment with user needs and enhance the efficacy of our AI solutions.

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Give an example of a challenging project you worked on involving AI infrastructure.

One challenging project I undertook involved implementing a real-time inference system that required low latency and high throughput. I collaborated with cross-functional teams to integrate advanced optimizations and utilized distributed systems, which ultimately led to improved performance metrics and positive end-user feedback.

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How do you stay updated with the latest advancements in AI and ML?

I stay updated with the latest advancements in AI and ML by attending industry conferences, participating in online courses, and following leading research papers and journals. I also engage with communities and forums where I can exchange knowledge with other professionals in the field.

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EMPLOYMENT TYPE
Full-time, hybrid
DATE POSTED
March 13, 2025

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