1 day ago
Base Salary
$125k - $195k/yr
Responsibilities
- Build AI systems and applications for engineering and business problems while defining measurable success criteria with users.
- Develop evaluations, benchmarks, datasets, automated graders, and scoring methods for model and agent performance.
- Create reproducible training and evaluation environments with task definitions, tool interfaces, constraints, and feedback signals.
- Integrate models and agents with engineering tools, internal services, and company data while capturing traces and results for debugging.
- Run experiments, establish baselines, analyze failures, and improve data, models, tools, and workflows.
- Develop data preparation, training, and evaluation workflows using targeted data collection, labeling, augmentation, and synthetic data where appropriate.
- Use Docker and Kubernetes to operate reliable, isolated, access-controlled, observable, and reproducible workloads.
- Collaborate with engineering, infrastructure, security, HR, and legal partners on data handling, auditability, traceable outputs, and human review.
- Maintain deployed systems through testing, monitoring, ongoing evaluation, and operational debugging.
- Work extended hours and weekends as needed.
Requirements
- Bachelor’s degree in computer science, data science, engineering, mathematics, or physics, or 4+ years of professional experience building and training AI/ML systems in lieu of a degree.
- 1+ years of Python programming and software development and debugging on Linux.
- 1+ years of experience with an ML framework such as PyTorch or practical experience developing agents and model-based applications.
- 1+ years of experience building or evaluating AI/ML systems through professional work, internships, research, or substantial independent projects.
- Understanding of evaluation metrics, baselines, training/evaluation data separation, error analysis, and reproducible experimentation.
- Experience with Docker and familiarity with Kubernetes.
- Familiarity with version control, software testing, and collaborative development practices.
- Preferred experience includes designing task-specific evaluations, evaluation datasets, automated graders, reinforcement learning environments, agent tools, reward functions, verifiers, model fine-tuning, reinforcement learning, human feedback, limited-data ML systems, Kubernetes workloads, experiment tracking, backend services, APIs, databases, sensitive-data systems, audit trails, and taking projects from requirements through deployment.
Benefits
- Base salary range is $125,000-$160,000 for Level 1 and $145,000-$195,000 for Level 2.
- Potential long-term incentives, long-term cash awards, discretionary bonuses, and employee stock purchase plan access.
- Medical, vision, dental, 401(k), short- and long-term disability, life insurance, paid parental leave, discounts, and other perks.
- Approximately three weeks of paid vacation, 10 or more paid holidays, and paid sick leave according to company policy.
Tech Stack
Categories
About SpaceX
SpaceX designs, manufactures, and launches orbital rockets and spacecraft, and operates Starlink, a global satellite internet network for consumers, businesses, and governments. Its revenue comes from commercial and government launch services (Falcon 9/Falcon Heavy, Dragon cargo and crew to the ISS) and subscription broadband with Starlink hardware and service. Founded in 2002 and headquartered in Hawthorne, California, the privately held company serves NASA and commercial satellite operators, building most systems in-house.