Staff Software Engineer, Vessel Performance Modeling

The Company

Sofar is the leading ocean intelligence platform. We’ve built the world’s largest real-time ocean network, turning billions of measurements into insights trusted by scientists, governments, and shipping fleets.  Our technology makes the ocean more predictable and sustainable, helping customers save costs, cut emissions, and unlock insights that were once out of reach.

The Role

You will be responsible for developing on our vessel performance modeling system designed to scalably model a full diversity of real world vessels at scale with our blend of physics-based principles with data-driven techniques. You will be partnering with our existing team of data scientists and software engineers directly improving our understanding of how vessels interact with the ocean environment, enabling decarbonization, cost savings, and safer maritime navigation.

Impact You’ll Own:

• Architect and engineer robust, scalable systems for modeling vessel performance under real-world conditions, including hydrodynamics, engine characteristics, weather impacts, and operational constraints.
• Set technical strategy and modeling direction in collaboration with Product teams to meaningfully impact our customers daily operations.
• Lead the development of hybrid models that combine physical simulation with machine learning and observational data from ships and sensors.
• Ensure models are well-tested, explainable, and production-ready, enabling reliable integration into customer-facing applications and APIs.
• Mentor team members and establish best practices for the balance of physical modeling, numerical methods, product scope and system reliability.

About You

• 10+ years of experience building production-grade modeling or simulation systems utilizing a hybrid of physics and data methods.
• Strong programming skills in Python and a system level language such as C++ or Rust.
• Track record of designing and owning large-scale modeling pipelines, preferably in real-world noisy data environments.
• Skilled at balancing scientific rigor with practical engineering constraints.
• Excellent communication skills, you can work across disciplines and explain complex systems to varied audiences.
• A physics or natural sciences undergraduate experience or equivalent.
• Passionate about the ocean, sustainability, and making a tangible impact on climate and logistics.

Bonus Points

• Experience in vessel performance modeling in the shipping industry.
• Experience in hydrodynamics or vessel dynamics.

Estimated Salary Range

• $170,000 - $220,000
• The range listed is what we reasonably expect to pay for this role at the time of this posting. We may ultimately pay more or less than the posted range and may be modified in the future. Actual compensation packages are based on several factors that are unique to each candidate, including but not limited to skill set, experience, and equity package.

Sofar's Commitment to Climate Justice

We at Sofar Ocean acknowledge that careers in the marine sciences “... have traditionally been, and remain, non-diverse work environments”, thereby limiting the entry and prosperity of underrepresented groups in the space. (Johri et al., 2021) Many of these same groups are disproportionately affected by climate change, and are often excluded from decision making that directly address their interests and needs.

We are committed to addressing these climate injustices and highly encourage people who identify as women, LGBTQ+, Black, Indigenous, and people of color (BIPOC) to apply.

Employee Conduct

It is the responsibility of every employee to contribute to a positive work environment through cooperative and professional interactions with co-workers, customers, and vendors.

Equal Employment Opportunity

All qualified applicants will receive consideration for employment without discrimination on the basis of race, color, religion, sex, sexual orientation, gender identity, national origin, protected veteran status, disability, or any other factors prohibited by law.