ML Engineer
5 Oct 2025
Verified by Turrior
Content + Source + Freshness • 11 Dec 2025 • 95% confidence
88 / 100
Offer value
Strong value due to the innovative focus on AI and machine learning with opportunities to influence the field significantly.
- Engage in pioneering AI and ML research.
- Significant impact on healthcare innovation and applications.
- Opportunity to shape the next generation of AI technologies.
Pros
- Opportunity to work on cutting-edge AI technologies.
- Direct involvement in shaping innovative solutions in healthcare.
- Collaboration with experienced founders in a startup environment.
Cons
- Fast-paced work environment may lead to high demands.
- Emerging technology means potential for rapid pivots and changes.
- Limited resources typical of early-stage startups might require flexibility.
Who it's for
Mid • Remote/Hybrid
Good fit
- ML engineers with a passion for research and development.
- Candidates who thrive in innovative and fast-paced environments.
- Professionals eager to work on real-world AI applications.
Not recommended for
- Individuals lacking practical machine learning experience.
- Those seeking a conventional corporate role.
- Professionals not willing to experiment with emerging technologies.
Motivation fit
Excitement for hands-on research and development.Desire to contribute to revolutionary applications in healthcare.Interest in a fast-paced, innovative startup culture.
Key skills
Expertise in ML frameworks like PyTorch or TensorFlow.Strong experimental design and analytical skills.Ability to collaborate with engineering teams on prototypes.Staying current with emerging AI methodologies.
Score: 88/100 AI verified analysis
About the job
The Role
We’re looking for an ML Engineer who’s passionate about applied research and eager to work on the frontier of AI agent development. You’ll bridge research and production — exploring, prototyping, and deploying models that push the limits of what autonomous agents can do in real-world settings.
Key Responsibilities
- Conduct applied research to evaluate, fine-tune, and adapt large language models and other AI architectures for our use cases
- Prototype experimental models and pipelines, testing novel approaches to agent orchestration, prompt design, or retrieval-augmented generation (RAG)
- Design and run experiments, analyze results, and iterate rapidly to improve agent performance
- Build robust, reproducible pipelines for data collection, preprocessing, and training
- Collaborate with backend and product engineers to operationalize research prototypes into production services
Requirements
- 2–4 years of experience in applied ML research or an industry role with a strong R&D component
- Solid foundation in machine learning fundamentals and experience working with LLMs or NLP models
- Proficiency in Python and ML frameworks such as PyTorch or TensorFlow
- Hands-on experience designing experiments, evaluating models, and analyzing data
- Familiarity with literature reviews and an eagerness to stay up-to-date with emerging techniques
Nice to Haves
- Experience with vector databases, embeddings, or semantic search
- Familiarity with reinforcement learning from human feedback (RLHF)
- Familiarity with agent frameworks
- Contributions to open-source ML projects or published research papers
Why You Should Join
- Competitive equity & pay - get in early and own what you build.
- Work closely with experienced founders with a proven startup track record.
- Move fast, ship fast - no corporate bureaucracy.
- Shape the AI revolution in healthcare - massive market, untapped potential
