Senior ML Engineer
5 Oct 2025
Verified by Turrior
Content + Source + Freshness • 11 Dec 2025 • 95% confidence
88 / 100
Offer value
The role offers significant influence over cutting-edge technology in AI and ML with a focus on impactful applications, strong compensation potential, and autonomy in shaping team practices.
- Lead in groundbreaking AI & ML initiatives
- Attractive equity opportunities
- Involvement in a fast-paced startup environment
- Requires significant ML expertise
Pros
- Opportunity to lead in a rapidly evolving AI technology space
- Exposure to advanced ML tools and infrastructure
- Potential for competitive equity compensation
Cons
- Requires high-level technical expertise and experience
- Fast-paced startup environment may demand long hours
- Limited initial structure may lead to ambiguity
Who it's for
Senior • Remote
Good fit
- Senior ML practitioners
- Tech innovators in AI
- Expert collaborators in fast-paced settings
Not recommended for
- Novice ML engineers
- Those averse to startup culture
- Candidates preferring rigid corporate environments
Motivation fit
Enthusiasm for pioneering AI applicationsDrive to influence healthcare with technologyA desire to mentor and contribute to team growth
Key skills
Machine Learning frameworks (TensorFlow, PyTorch)Model evaluation and A/B testingMLOps and containerization tools (Docker, Kubernetes)Collaboration with cross-functional teams
Score: 88/100 AI verified analysis
About the job
The Role
We’re looking for a Senior ML Engineer to lead the design, deployment, and optimization of large-scale models powering intelligent agents. Your role spans model architecture, operational deployment, and production monitoring.
Key Responsibilities
- Design, fine-tune, and evaluate large language models and neural networks for modular agent behavior
- Build robust ML pipelines (data ingestion, feature engineering, model training, serving, monitoring)
- Develop containerized model serving infrastructure (Docker, Kubernetes), integrating with backend APIs
- Implement evaluation frameworks, A/B testing, and performance metrics to quantify agent effectiveness
- Ensure reproducibility, traceability, and compliance across ML lifecycle
- Collaborate with backend engineers to define inference service SLAs and efficient real-time ML delivery
- Mentor junior ML teammates and establish team-level best practices
Requirements
- 5+ years as an ML Engineer or applied ML researcher with production model deployment experience
- Strong Python skills, experience with ML frameworks (TensorFlow, PyTorch) and LLM tooling (HuggingFace)
- Expertise in MLops: Docker, Kubernetes, model serving (e.g., Triton, FastAPI), CI/CD
- Familiarity with data pipelines, SQL, cloud platforms (AWS SageMaker, GCP Vertex, Azure ML)
- Solid understanding of model evaluation, A/B testing, and ML performance metrics
- Excellent collaboration skills with product, backend, and data teams
Nice to Haves
- Prior work with conversational agents, retrieval-augmented generation, or multi-model orchestration
- Experience with vector search stacks (e.g. Pinecone, FAISS)
- Knowledge of embedding techniques, prompt engineering, or Reinforcement Learning from Human Feedback (RLHF)
- Startup experience and ability to navigate ambiguity and shape technical direction
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
