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Core Competencies
Role fitCore Competencies
Use this summary to align your resume positioning with the role.
Demonstrates expertise in building agentic systems and applying machine learning models in complex environments, with strong software engineering skills in Python and hands-on experience with PyTorch. Capable of translating customer needs into reusable platform capabilities while managing model releases and production trade-offs.
Highest-signal resume keywords
Machine Learning ResearchAgentic Systems DevelopmentPython Software EngineeringPyTorch FluencyModel Serving and APIs
ATS Keywords
Tailor your resumeApplicant Tracking System Keywords
Tip: use these terms in your resume and cover letter to boost ATS matches.
Hard Skills
Machine LearningIn-Context LearningAgentic SystemsModel EvaluationObservabilityReliabilityProduction Trade-OffsModel ServingAPIsCloud Infrastructure
Soft Skills
JudgmentOutcome-Oriented MindsetCollaborationCustomer EngagementStakeholder Communication
Tools & Technologies
Weights & BiasesModel RegistriesCI for ModelsMLOps ToolingSDK Design
Industry Keywords
Synthetic DataProbabilistic ModelsStartup ExperienceProduct ExperienceResearch-Adjacent Engineering
Tech Stack
Tools & technologiesCloudPythonPyTorch
About the role
Key responsibilities & impact- Turn research models into sophisticated agentic systems capable of a large degree of autonomy
- Build harnesses, inference algorithms and systems, and smart context curation
- Build release criteria that quantitatively show when an agent is ready to ship
- Build developer-facing abstractions around agentic systems
- Work closely with researchers to expose failure modes
- Receive product feedback from customers and turn it into better systems
- Translate customer and design-partner needs into reusable platform capabilities rather than one-off solutions
- Own model releases, monitoring and rollback patterns as the production footprint grows
- Work between research and product, then hand systems over to the productization team
Requirements
What you’ll need- A track record of ML-research or strong, research-adjacent ML engineering
- Experience with in-context learning
- Experience building agentic systems
- Experience applying ML models in complex, dynamic environments
- Strong software engineering skills in Python
- Hands-on fluency with PyTorch
- Sound judgment around evaluation, observability, reliability and production trade-offs
- Ability to work directly with customers, researchers and product stakeholders
- Pragmatic, outcome-oriented mindset
- Primarily hiring at senior level; exceptional candidates with fewer years of experience may be considered if they demonstrate comparable depth, judgment and ownership
- Nice to have: PFNs, synthetic data or probabilistic models
- Nice to have: Weights & Biases, model registries, CI for models or comparable MLOps tooling
- Nice to have: SDK or developer-tooling design
- Nice to have: Prior startup, design-partner or 0-to-1 product experience
- Experience with model serving, APIs, containers and cloud infrastructure
Benefits
Comp & perks- VSOP equity: a real stake in what we build
- 30 days of paid holiday per year
- Statutory social insurance
- Conference travel and role-relevant learning
- Flexible hybrid work, with roughly one in-person team meet-up per month
- A high-end laptop
- Access to the cloud compute required for the role
- Potential expansion into technical ownership of the model-to-product stack or leadership of a small ML product group