Machine Learning Engineer

About Dirac Labs

Dirac Labs is building diamond NV-center magnetometers and related quantum sensors that unlock precise navigation when GPS is unavailable. Our work is supported by NASA, NOAA, Emergent Ventures, and the USISTEF. Our mission is simple: provide trustworthy navigation in every location without worrying about spoofing or jamming.

Why this role exists

We are creating foundation models for magnetic navigation. The goal is a unified model that consumes streams of magnetic data, actively denoises them, matches to prior maps, and outputs one’s location in real time.

What will you do

• Design and train denoising models for magnetic signals across diverse environments and platforms.
• Build map-matching pipelines that align live magnetic observations with prior magnetic maps to infer pose and position.
• Architect a unified model that fuses temporal magnetic data, inertial cues, and contextual priors to deliver real-time location with uncertainty bounds.
• Create data tooling for collection, labeling, augmentation, and synthetic generation of magnetic trajectories.
• Own the evaluation stack: metrics, offline replay, A-B experiments, on-device latency and power profiling.
• Productionize models on embedded and edge targets, integrating with our flight computer and navigation stack.
• Collaborate closely with quantum hardware, firmware, and field ops to close the loop between model assumptions and real-world behavior.
• Publish internal specs, ablation studies, and decision records that make the system understandable and maintainable.

You might be a fit if you have

• 2+ years in applied ML or a strong research background with shipped systems.
• Depth in sequence modeling or sensor fusion. Examples include Transformers for time series, state space models, Kalman filtering variants, or diffusion-style denoisers.
• Experience building real-time inference systems. Comfort with quantization, pruning, and profiling.
• Strong Python and one systems language. Preferably C++.
• Practical instincts around data. From designing field runs to cleaning, aligning, and stress-testing datasets.
• Clear writing and a habit of turning experiments into decisions.

Nice to have

• Prior work on SLAM, map-matching, or PNT.
• Embedded or edge ML deployment on NVIDIA Jetson, Qualcomm, or ARM targets.
• Time series foundation models, contrastive pretraining, self-supervised learning at scale.
• Synthetic data generation, domain randomisation, or physics-informed learning.
• Experience with inertial sensors, magnetometers, or geophysics datasets.

Location

Bengaluru, India. In-person by default. Occasional field testing.

Compensation

Competitive salary, meaningful equity, and the chance to define the company’s navigation intelligence from day one.