{"id":68649,"url":"https://alion.io/job/andromeda-forward-deployed-engineer-sre","title":"Forward Deployed Engineer - SRE","company":{"id":27148,"name":"Andromeda","domain":"andromeda.ai","url":"https://alion.io/company/andromeda","size_band":null,"is_staffing_agency":false,"employer_type":"direct","is_intermediary":false,"listed_via":null,"ats_vendor":"Ashby","truth_index":{"grade":"A","score":93,"open_postings":12,"ghost_share":0,"stale_share":0.5,"repost_share":0,"time_to_fill_p50_days":13,"computed_at":"2026-10-17T05:46:00Z"}},"role":"Solutions","role_family":"Solutions","seniority":null,"employment_type":"full_time","work_mode":"remote","remote_scope":"stated_regions","remote_scope_basis":"posting_text","remote_working_hours":null,"hiring_geo_confidence":"inferred","locations":["San Francisco, United States"],"countries":["US"],"hiring_countries":["US","CA","MX","AG","AW","BS","BB","BZ","BM","VG","BQ","KY","CR","CW","DM","DO","SV","GL","GD","GP","GT","HT","HN","JM","MQ","MS","NI","PA","PR","KN","LC","PM","SX","TT","TC","VI"],"hiring_countries_total":36,"salary":null,"salary_estimate":{"min_usd":114000,"max_usd":249000,"period":"year","method":"role_country_seniority_unknown","sample_n":4100},"experience_years_min":null,"visa_sponsorship":false,"relocation_package":false,"has_equity":false,"technologies":[{"name":"Ansible","optional":false},{"name":"Bash","optional":false},{"name":"CUDA","optional":false},{"name":"CUDA Toolkit","optional":false},{"name":"Helm","optional":false},{"name":"HPC","optional":false},{"name":"InfiniBand","optional":false},{"name":"Kubernetes","optional":false},{"name":"KV Cache","optional":false},{"name":"Linux","optional":false},{"name":"NCCL","optional":false},{"name":"NVLink","optional":false},{"name":"Python","optional":false},{"name":"Self-Healing","optional":false},{"name":"Slack","optional":false},{"name":"SLURM","optional":false},{"name":"Terraform","optional":false}],"status":"live","first_seen_at":"2026-07-31T19:23:35Z","employer_posted_date":"2026-07-31","last_verified_at":"2026-10-18T07:11:28Z","board_verified":true,"closed_at":null,"days_open":78,"trust":{"level":"ok","repost_count":0,"flags":[],"days_open":78},"description":"Forward Deployed Engineer - SRE\nLocation: North America Remote/SF-Hybrid · Full-Time\nAbout Andromeda\nAndromeda is a market and infrastructure platform to buy, sell, and operate compute.\nWe believe demand for compute will grow exponentially. So fast that a handful of vertically integrated providers won't be able to scale across operations, capital, supply chains, and politics to serve it. The result is a massive wave of fragmentation, with AI factories of every shape and size coming to market to fill this demand. Our job is to enable all of that fragmented compute to flow through one platform, delivering reliable capacity to model builders, research labs, and inference providers when they need it. We believe every spare electron should be made productive for AI and we're building the platform that makes that possible.\nWe sit at the center of three forces:\nCompanies that need reliable, high-performance compute fast\n\nA fragmented global supply of GPUs across hyperscalers, neoclouds, and independent data centers\n\nCapital, risk, and operational complexity that most teams are not equipped to manage\n\nWhen we succeed, trillions of dollars of compute will flow through Andromeda. Builders get capacity when they need it. Providers get a reliable way to monetize, operate, and finance infrastructure at scale. Capital gets an easy way to deploy, hedge, and underwrite.\nIn five years, Andromeda won't just participate in the AI infrastructure market. We will shape it.\nThe Role\nThis is not a generalist SRE role, and it is not a support role. You will embed directly with the teams running large-scale training and inference on our clusters. You are responsible for onboarding them, tuning their jobs, and debugging their failures alongside them, while owning the infrastructure and automation that makes those clusters reliable in the first place.\nForward deployed means you spend real time inside customer environments: reading their training code, sitting in their Slack channels, watching their runs, and shipping fixes that land in our platform. When a multi-hundred-GPU run stalls, you are the person who figures out whether it's the fabric, the driver, the scheduler, or their dataloader, then you make sure it can't happen the same way twice.\nWe're looking for engineers who have personally run GPU clusters in production, understand the failure modes of distributed training, and can reason about performance from network fabric → kernel → framework. Equally important: you can explain what you found to someone else's engineering team without condescension, and turn that conversation into a product improvement.\nWhat You’ll Do\nServe as the primary technical point of contact for teams running large-scale training and inference workloads. Own onboarding end to end; environment setup, orchestration choice (Slurm, Kubernetes, or direct SSH), storage layout, first successful run at scale. You will continue to stay engaged as their workloads grow.\n\nWork inside customer environments to diagnose real failures: NCCL timeouts, stragglers, checkpoint I/O stalls, degraded links, OOM patterns, container and driver mismatches. Read their code when you need to. Reproduce, isolate, fix, and write it down.\n\nProfile and improve distributed training performance on live workloads. Improving MFU, cutting idle GPU time, and reducing time-to-first-successful-run for new deployments.\n\nOwn reliability outcomes for the accounts you're deployed on.\n\nEnsure the health and performance of high-speed interconnects (InfiniBand, RoCE, NVLink) that underpin distributed training. Diagnose and resolve fabric-level issues that degrade collective operations.\n\nBuild deep visibility into GPU utilization, memory pressure, interconnect throughput, job performance, and hardware health.\n\nTurn every repeated deployment problem into automation: cluster provisioning, GPU health checks and burn-in, preflight validation, self-healing, firmware/driver lifecycle management, and reusable reference configurations for common training and serving stacks.\n\nLead incident response for complex, multi-layer failures spanning hardware, networking, orchestration, and ML frameworks. Own the customer-facing communication during the incident and the blameless postmortem and systemic fix after it.\n\nYou will see our rough edges before anyone else does. Bring that signal back to influence the roadmap, file the hard bugs, and build the missing pieces yourself when that's the fastest path.\n\nWhat We’re Looking For\nHands-on experience operating GPU clusters in production (NVIDIA A100/H100/H200/B200 or equivalent). You understand GPU memory hierarchies, ECC behavior, thermal throttling, and hardware failure modes from direct experience.\n\nProduction experience with InfiniBand, RoCE, or NVLink fabrics in the context of distributed training. You can diagnose why an all-reduce is slow, identify a degraded link in a fat-tree topology, and reason about congestion control at scale.\n\nWorking knowledge of how large training and inference jobs actually run. You don't need to design models, but you need to understand what's happening at the systems level when a large run stalls.\n\nExpert-level Linux experience: kernel tuning, driver management (NVIDIA drivers, CUDA toolkit), cgroup/namespace internals, container runtimes, and performance profiling at the syscall and hardware level.\n\nStrong experience running Kubernetes in production with GPU workloads. Experience with device plugins, topology-aware scheduling, multi-cluster, custom operators. Experience with Slurm or other HPC schedulers is equally valued.\n\nStrong engineering skills in Python, Go, or Bash. You build production-grade tools and services, not just scripts.\n\nInfrastructure-as-Code proficiency (Terraform, Helm, Ansible, or equivalent).\n\nHands-on experience building monitoring and alerting for GPU-specific telemetry (DCGM, nvidia-smi, fabric manager metrics) integrated into actionable dashboards.\n\nYou can go deep on architecture with a customer's infra team and clearly articulate tradeoffs to their leadership. You're comfortable being the only one in the room who knows the answer, and equally comfortable saying you don't yet.\n\nProven track record leading incident response for complex distributed systems.\n\nStrong Candidates May Have\nExperience with high-performance parallel file systems (VAST, WEKA, Lustre, GPFS) and the checkpoint I/O and data-loading bottlenecks that come with large training runs.\n\nTime spent embedded with external engineering teams, i.e solutions architecture, professional services, deployed SRE, or technical account ownership at an infrastructure company.\n\nExperience operating production inference. Hands on experience with autoscaling, batching, KV cache behavior, cold starts, and multi-tenant GPU sharing.\n\nContributions to relevant OSS projects, or benchmarks, postmortems, and deep-dives you've published.\n\nExperience working across heterogeneous providers and regions rather than a single hyperscaler.\n\nWhat Success Looks Like\nBy the end of your first year:\nYou know each of your accounts' actual technical goals including what they're training, what their scaling roadmap looks like over the next two quarters, what their real constraints are (budget, deadline, headcount, data), and you've written that down somewhere the rest of us can read it.\n\nYou are the person your accounts' engineers message first, before they file a ticket, because you've earned it.\n\nTheir reliability and throughput numbers are visibly better than at onboarding, and you can point to the specific changes that did it.\n\nRecurring problems you found in the field exist as automation, preflight checks, or documentation, not as tribal knowledge in your head.\n\nYou've advocated internally for at least one roadmap change on behalf of a strategic customer, and it shipped.\n\nWhy You’ll Love It Here\nHigh-growth environment: Get in early at a company at the center of the AI infrastructure boom\n\nOwnership: First FDE for the solutions engineering team, you’ll get to build this function from the ground up\n\nCompetitive compensation: + meaningful equity\n\nComprehensive benefits: for you and your dependents, including healthcare, dental, and vision coverage, 401(k), and unlimited PTO\n\nAndromeda Cluster is an equal opportunity employer. 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