Senior/Principal Algorithms Engineer, Power Applications

Karman is a fast-growing NVIDIA-backed AI company enabling AI data centers to dynamically orchestrate power and unlock more compute capacity from existing energy infrastructure. For over a decade, we have applied AI to the electric grid, bringing real-time visibility and power-flow control to complex energy infrastructure. Our Karman platform, built on a custom NVIDIA module, brings that same capability to AI data centers, giving operators a way to better use the power already available to them.

The Power Applications team takes power orchestration algorithms from idea to production. We develop, ship, and maintain the applications that run on Karman to orchestrate closed-loop control actions and deliver quantifiable customer value. We run two workflows in parallel. Research and development proves and matures new control, estimation, and forecasting capabilities and builds the evidence to decide what moves into the product. Execution and continuous delivery turns that validated theory into reliable, deployable software and keeps it performing in the field. Every improvement we ship is earned through experiments on real GPU hardware and realistic workloads, where we quantify and optimize electrical compliance, power capacity utilization, and compute throughput.

We are hiring several senior algorithms engineers into Power Applications through this one posting, and we are keeping the profile deliberately broad. Everyone we hire works on the same problem: allocating and enforcing power across servers, racks, and rows at high speed and with hard reliability guarantees. Where you spend most of your time depends on your strengths:

• Theory and prototyping: developing the models, estimators, and control strategies behind the next generation of the product
• Production delivery: implementing, validating, and tuning algorithms in production code alongside our software engineers, and owning their performance once deployed
• Experimentation: designing, running, and analyzing the experiments that decide which ideas ship, and turning results into decisions

Most people will lean toward one of these and contribute to all three, and we will place you where you will have the most impact. Day to day you will work with the principal engineers who own our control architecture and our production algorithm performance.

This is a hands-on, end-to-end role. Whatever your focus, you see your work through from idea to field performance rather than handing it off, and you stay with it release after release, measuring, tuning, and improving.

You do not need to match every qualification below to apply. Strong candidates come from many paths: control theory and estimation, building and hardening real-time systems, or experimental science on physical systems. If you bring rigor, a systems mindset, and the drive to see your work shipped and owned, we want to hear from you.

Responsibilities

• Design, analyze, and deliver improvements to the control and orchestration algorithms that allocate and enforce power across servers, racks, and rows at high speed and with hard reliability guarantees
• Own algorithms end to end: theory and prototyping (Python), production implementation (Rust and Python), validation, deployment, and ongoing tuning in the field
• Help to define how we measure algorithm performance, from adherence to electrical limits to impact on compute throughput, and build the benchmark and regression suites that hold each release to that bar
• Continuously improve the performance, robustness, and operability of the algorithm-driven services already in production
• Drive R&D that de-risks and shapes future product iterations (forecasting, automated parameter tuning, orchestration at larger scales, new compute and actuation hardware), running experiments in our lab and pilot environments and turning results into clear decisions

Minimum Qualifications

• Bachelor's degree in electrical engineering, computer science, applied mathematics, physics, mechanical or aerospace engineering, or a related quantitative field, or equivalent practical experience
• Deep expertise in control systems and mathematical optimization, including feedback control, state estimation, and the practical realities of latency, noise, and missing data, ideally in distributed or edge settings
• A track record of taking algorithms from concept to production in physical systems, owning their performance once deployed, and designing them to fit the whole system rather than optimizing components in isolation
• Strong Python skills for prototyping and analysis (e.g. pyspark, tensorflow, pytorch) and the ability to work in production codebases in a systems language (we use Rust, and will support you in learning it)
• Comfort in a continuous delivery environment: working directly in production repositories, writing tests, reviewing code, and using field performance data to understand and improve algorithms
• Excellent written communication and the habits of a strong remote teammate: proactive updates, thorough documentation, empathy, and assuming good intent
• Willingness to travel up to 10% of the time

Enhanced Qualifications (Nice to Have)

• Advanced degree (MS or PhD) in control, electrical or mechanical engineering, applied mathematics, robotics, computer science, or a related field, or similar research experience
• Background in power systems or data center electrical infrastructure, including protection, power quality
• Experience with time series forecasting, or learning-based control, including online learning and re-enforcement learning frameworks
• Experience with GPU power management (NVML, DCGM, or BMC/Redfish) or with LLM inference serving and its telemetry
• Experience with LLM inference stacks (e.g vLLM, Dynamo) 
• Publications or open-source contributions in state estimation, optimization, or control
• Experience driving model development workflows in DataBricks, including exploratory data analysis, feature extraction, and model prototyping

Salary Range: $170,000 to $220,000 base compensation depending on experience plus stock options. Salary will be commensurate with an individual's skills, training, years of experience, and in line with internal compensation bands.

Location: This position can be performed remotely from anywhere in the United States. Preference will be given to candidates based in Michigan with the ability to work on site in Utilidata’s headquarters.

Our Commitments: Karman values the diversity of our team. We provide equal employment opportunities without regard to race, color, religion, creed, sex, gender, sexual orientation, gender identity or expression, national origin, age, physical disability, mental disability, medical condition, pregnancy or childbirth, sexual orientation, genetics, genetic information, marital status, or status as a covered veteran or any other basis protected by applicable federal, state and local laws.

We are committed to:

• Creating a diverse and inclusive workplace that is welcoming, supportive, affirming and respectful
• Empowering employees to solve problems and work together to make a difference
• Providing mentorship and growth opportunities as part of a collaborative team
• A flexible work environment with flexible paid time off
• Competitive compensation and benefits, including health, dental, vision, and employer-match 401k