About the role
structured by ORIKey Accountabilities JOB DESCRIPTION Build ML solutions for decision-making problems: planning, sequencing, routing, allocation, and resource utilization. Prototype fast using agentic coding tools (e.g., Claude Code-style workflows): generate scaffolds, refactor, write tests, iterate on experiments—while maintaining…
What you will do
- Build ML solutions for decision-making problems: planning, sequencing, routing, allocation, and resource utilization.
- Prototype fast using agentic coding tools (e.g., Claude Code-style workflows): generate scaffolds, refactor, write tests, iterate on experiments—while maintaining strong engineering discipline.
- Develop and evaluate models in areas like optimization & solvers, Deep RL / Decision Intelligence, and predictive ML.
- Design robust evaluation harnesses: offline simulation, counterfactual testing, ablations, and scenario analysis; define KPIs and acceptance thresholds.
- Collaborate with ML engineers to support productionization: latency/throughput constraints, monitoring, reproducibility, model versioning, and safe rollout.
What they are looking for
- 0–5 years experience in applied ML / data science / applied research (internships, thesis work, and strong project portfolios count).
- Demonstrated experience using agentic coding assistants in real development (e.g., Claude Code, similar agentic coding environments) to accelerate iteration—without sacrificing code quality.
- Strong Python skills and comfort with ML tooling (PyTorch preferred; TensorFlow ok).
- Solid foundations in algorithms, probability/statistics, and experimental design.
- Ability to translate messy real-world problems into clear formulations and measurable success metrics.
Nice to have
- Prior work in Deep RL (a strong differentiator), such as: PPO/SAC/DQN style methods, offline RL, imitation learning, MCTS/planning hybrids, or building environments/simulators, reward design, stability/debugging, evaluation
- Experience with simulation-based evaluation or digital twins (even lightweight simulators).
- Familiarity with MLOps basics: MLflow, Docker, CI/CD, model monitoring.
- Domain exposure to logistics/supply chain/industrial operations (nice-to-have, not required).
Full posting text
Key Accountabilities
JOB DESCRIPTION
Build ML solutions for decision-making problems: planning, sequencing, routing,
allocation, and resource utilization.
Prototype fast using agentic coding tools (e.g., Claude Code-style workflows):
generate scaffolds, refactor, write tests, iterate on experiments—while maintaining
strong engineering discipline.
Develop and evaluate models in areas like:
○ Optimization & solvers: MILP/CP-SAT, heuristics/metaheuristics, constraint
programming, search methods
○ Deep RL / Decision Intelligence: RL baselines, offline RL, bandits,
MCTS-style planning, policy/value learning
○ Predictive ML: forecasting and estimation models that feed decision systems
Design robust evaluation harnesses: offline simulation, counterfactual testing,
ablations, and scenario analysis; define KPIs and acceptance thresholds.
Collaborate with ML engineers to support productionization: latency/throughput
constraints, monitoring, reproducibility, model versioning, and safe rollout.
Write clear technical documentation and communicate findings to both technical and
non-technical stakeholders.
What We’re Looking For (Required)
0–5 years experience in applied ML / data science / applied research (internships,
thesis work, and strong project portfolios count).
Demonstrated experience using agentic coding assistants in real development
(e.g., Claude Code, similar agentic coding environments) to accelerate
iteration—without sacrificing code quality.
Strong Python skills and comfort with ML tooling (PyTorch preferred; TensorFlow ok).
Solid foundations in algorithms, probability/statistics, and experimental design.
Ability to translate messy real-world problems into clear formulations and measurable
success metrics.
Strong Plus / Preferred
Prior work in Deep RL (a strong differentiator), such as:
○ PPO/SAC/DQN style methods, offline RL, imitation learning, MCTS/planning
hybrids
○ Building environments/simulators, reward design, stability/debugging,
evaluation
Experience with simulation-based evaluation or digital twins (even lightweight
simulators).
Familiarity with MLOps basics: MLflow, Docker, CI/CD, model monitoring.
Domain exposure to logistics/supply chain/industrial operations (nice-to-have, not
required).
Tools & Tech (Indicative)
Python, PyTorch, OR-Tools / solver stacks, RL libraries (Ray RLlib / Stable Baselines), SQL,
Docker, Git, MLflow; cloud platforms a plus.
Employment type: Full-time
Job function: Other
Industries: Transportation, Logistics, Supply Chain and Storage