Senior Modeling & Optimization Engineer

Mytra; United States; On-site; USD 150,000 - 210,000; Full time

Benefits offered by this job

Competitive compensation and equity

Fully subsidized health coverage

401(k) plans

Fully subsidized lunch and snacks atHQ

PTO & holidays

Commuter benefits

Job summary

Mytra is hiring for a delivery-focused role within the Modeling & Optimization team. You will own analytical work end-to-end on customer engagements, using simulation, optimization, and data science to shape warehouse designs and validate automation solutions.

Balancing speed and fidelity, you will build tools that empower solutions engineers and designers to self-serve routine analyses, and you will interact with customers to inform decisions and product requirements.

Qualifications

• 5+ years of industry or relevant experience.
• Strong proficiency in Python, including production-grade code.
• Experience with at least two of: discrete-event simulation, mathematical optimization, statistical modeling, or applied data science.

Responsibilities

• Own analytical delivery on customer engagements end-to-end with autonomy.
• Work across two modeling modes for rapid design iteration and high-fidelity validation.
• Analyze operational data to inform system design and sizing decisions.
• Design, build, and execute simulation models to validate warehouse designs and quantify performance.
• Develop optimization models and recommender logic for layout decisions and fleet sizing.
• Build self-serve tooling so teams can run routine analyses independently.
• Translate customer signals into product requirements for the broader org.
• Conduct experiments, sensitivity analyses, and communicate actionable results.

Skills

Python

Autonomy

Ownership of analytical work

Discrete-event simulation

Mathematical optimization

Statistical modeling

Applied data science

Version control

Tools

SimPy

Plotly

Matplotlib

Dash

Job description

About the role

You’ll own analytical delivery on Mytra’s customer engagements — using operations research methods (simulation, optimization, and data science) to solve customer design problems and validate warehouse automation solutions. This is a delivery-owner role, not a support seat: you’ll take engagements end-to-end — analyzing operational data, modeling system behavior, running design experiments, and translating findings into recommendations that land deals and shape what Mytra builds next — with the autonomy and judgment to run them without close direction.

A defining part of the job is working across two modeling modes and knowing when to use each: fast, low-setup models for rapid design iteration early in a deal, and deeper, higher-fidelity validation when a decision warrants it. Balancing speed against fidelity — and building the tooling that lets others run the fast mode themselves — is central to the role.

This role sits within the Modeling & Optimization team, the technical and analytical backbone of Mytra’s commercial pipeline. M&O’s mandate is to make the commercial org continuously better — and a central way we do that is by building tools that hand capability back to solutions engineers and designers so analytical work isn’t gated on our team. You’ll both deliver the hard analysis and work yourself out of being the bottleneck on the routine parts.

What you’ll do:

• Own analytical delivery on customer engagements end-to-end — from operational data through to recommendations — with the judgment to scope depth appropriately and run engagements without close direction.


• Work across two modeling modes: fast, low-setup models for rapid design iteration, and deeper planner-in-the-loop validation for higher-fidelity de-risking — and exercise judgment on when each is warranted.


• Analyze customer operational data — order profiles, SKU demand patterns, throughput characteristics, and work schedules — to inform system design and sizing decisions.


• Design, build, and execute simulation models (discrete-event simulation, scoped scenario models) to validate warehouse designs and quantify system performance for customer proposals.


• Develop optimization models and recommender logic to support layout decisions, fleet sizing, zoning strategies, and other design trade-offs.


• Build and hand off self-serve tooling to the commercial org — so solutions engineers and designers can run routine analysis themselves and the team is not the bottleneck on every study.


• Make sense of data across customers and solutions, and help translate what we see on the customer side into product requirements — so this signal reaches product without solutions engineers carrying the full analytical load.


• Conduct structured experiments, sensitivity analyses, and design space explorations; communicate results and actionable recommendations to technical and non-technical audiences.


• Build repeatable analytical workflows, scenario configurations, and validation frameworks that raise the baseline capability of the broader commercial engineering org.

Ideal Candidates

• 5+ years of industry or relevant experience.


• A track record of owning analytical work end-to-end and operating with autonomy — comfortable being accountable for delivery, not just contributing to it.


• Strong proficiency in Python, including object-oriented design and building production-grade code.


• Experience with at least two of: discrete-event simulation, mathematical optimization, statistical modeling, or applied data science.


• Range, and an appetite for it: this role flexes across analysis, tooling, and process as the commercial org’s needs evolve, and you’re energized rather than frustrated by work that changes shape.


• A bias toward building yourself out of repetitive work — you measure success partly by how much you’ve enabled others to self-serve.


• Ability to design experiments, analyze data, and communicate insights clearly to technical and non-technical audiences.


• Solid engineering fundamentals (systems thinking, algorithmic reasoning, version control, testing).


• Comfort working directly with customers or in customer-adjacent roles where your analysis informs high-stakes decisions.

Nice to have:

• Professional experience in simulation engineering or modeling/operations research teams.


• Familiarity with DES frameworks (SimPy, salabim, internal engines, or equivalent).


• Background in warehouse automation, robotics, manufacturing systems, industrial engineering, or related fields.


• Experience with data visualization (Plotly, Matplotlib, Dash, etc.)


• Experience contributing to simulation platforms, modeling libraries, or internal tooling.

Benefits Include:

• Competitive compensation and equity grants at a high-growth company backed by top-tier VCs


• Fully subsidized health coverage, including medical (baseline plans), dental, and vision for employees and dependents


• 401(k) plans and employer-subsidized life insurance


• Fully subsidized lunch and snacks at HQ—we eat and share stories together at the “long” table


• Generous PTO and company-paid holidays, including one week over the winter break so everyone can recharge together


• Voluntary pet insurance, Voluntary Life Insurance, Accident, Critical Illness, and Hospital Indemnity


• Fully subsidized tax advisory services and education to help you understand your equity


• Commuter benefits, including a up to $150 monthly commuter benefit


• Lively, modern combined office and lab space where we rapidly iterate through design, build, and test phases


• Fully equipped onsite gym and showers at headquarters

Pay Notice

Compensation for this position will be set based on the candidate’s experience level and location. The posted salary band is applicable only to the San Francisco Bay Area and is subject to change.